{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/federated-learning/papers/ran/2","list_of":"/task/federated-learning","task":"Federated Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":5,"rows_per_page":100,"rows":[101,200],"of":457,"counts":{"archive_papers_tagged":6771,"with_a_code_link":1815,"where_syntology_ran_a_sample":457,"not_listed_spam_title":0,"listed":6771,"listed_where_code_ran":457,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":380,"every_run_a_failure_of_syntologys_instrument":77,"listed_with_a_run_with_no_instrument_failure":380,"listed_every_run_a_failure_of_syntologys_instrument":77,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/federated-learning/papers/ran/1","prev":"/task/federated-learning/papers/ran/1","next":"/task/federated-learning/papers/ran/3","papers":[{"url":"/paper/global-and-local-prompts-cooperation-via","slug":"global-and-local-prompts-cooperation-via","title":"Global and Local Prompts Cooperation via Optimal Transport for Federated Learning","date":"2024-02-29","arxiv_id":"2403.00041","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/global-and-local-prompts-cooperation-via#ran","syntology_url":"https://syntology.ai/paper/2403.00041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00041"}},"official":{"repos":["hongxialee/fedotp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-one-shot-federated-learning-through","slug":"enhancing-one-shot-federated-learning-through","title":"Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting","date":"2024-02-23","arxiv_id":"2402.15070","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-one-shot-federated-learning-through#ran","syntology_url":"https://syntology.ai/paper/2402.15070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15070"}},"official":{"repos":["rong-dai/co-boosting"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedlion-faster-adaptive-federated","slug":"fedlion-faster-adaptive-federated","title":"FedLion: Faster Adaptive Federated Optimization with Fewer Communication","date":"2024-02-15","arxiv_id":"2402.09941","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fedlion-faster-adaptive-federated#ran","syntology_url":"https://syntology.ai/paper/2402.09941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09941"}},"official":{"repos":["tzw1998/fedlion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedlps-heterogeneous-federated-learning-for","slug":"fedlps-heterogeneous-federated-learning-for","title":"FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing","date":"2024-02-13","arxiv_id":"2402.08578","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fedlps-heterogeneous-federated-learning-for#ran","syntology_url":"https://syntology.ai/paper/2402.08578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08578"}},"official":{"repos":["jyzgh/fedlps"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/differentially-private-decentralized-learning-1","slug":"differentially-private-decentralized-learning-1","title":"Differentially Private Decentralized Learning with Random Walks","date":"2024-02-12","arxiv_id":"2402.07471","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/differentially-private-decentralized-learning-1#ran","syntology_url":"https://syntology.ai/paper/2402.07471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07471"}},"official":{"repos":["totilas/dprandomwalk"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/clients-collaborate-flexible-differentially","slug":"clients-collaborate-flexible-differentially","title":"Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off","date":"2024-02-10","arxiv_id":"2402.07002","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clients-collaborate-flexible-differentially#ran","syntology_url":"https://syntology.ai/paper/2402.07002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07002"}},"official":{"repos":["6lyc/fedceo_collaborate-with-each-other"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/decentralized-sporadic-federated-learning-a","slug":"decentralized-sporadic-federated-learning-a","title":"Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees","date":"2024-02-05","arxiv_id":"2402.03448","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/decentralized-sporadic-federated-learning-a#ran","syntology_url":"https://syntology.ai/paper/2402.03448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03448"}},"official":{"repos":["ShahryarBQ/DSpodFL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/spectral-co-distillation-for-personalized-1","slug":"spectral-co-distillation-for-personalized-1","title":"Spectral Co-Distillation for Personalized Federated Learning","date":"2024-01-29","arxiv_id":"2401.17124","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spectral-co-distillation-for-personalized-1#ran","syntology_url":"https://syntology.ai/paper/2401.17124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17124"}},"official":{"repos":["jimmyc96/spectral-dis-fl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedloge-joint-local-and-generic-federated","slug":"fedloge-joint-local-and-generic-federated","title":"FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data","date":"2024-01-17","arxiv_id":"2401.08977","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":1,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fedloge-joint-local-and-generic-federated#ran","syntology_url":"https://syntology.ai/paper/2401.08977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08977"}},"official":{"repos":["zackzikaixiao/fedloge"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fedtgp-trainable-global-prototypes-with","slug":"fedtgp-trainable-global-prototypes-with","title":"FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning","date":"2024-01-06","arxiv_id":"2401.03230","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/fedtgp-trainable-global-prototypes-with#ran","syntology_url":"https://syntology.ai/paper/2401.03230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03230"}},"official":{"repos":["tsingz0/fedtgp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-class-incremental-learning-with-1","slug":"federated-class-incremental-learning-with-1","title":"PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning","date":"2024-01-04","arxiv_id":"2401.02094","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-class-incremental-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/2401.02094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02094"}},"official":{"repos":["ghy0501/pilora"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/harnessing-the-power-of-federated-learning-in","slug":"harnessing-the-power-of-federated-learning-in","title":"Harnessing the Power of Federated Learning in Federated Contextual Bandits","date":"2023-12-26","arxiv_id":"2312.16341","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/harnessing-the-power-of-federated-learning-in#ran","syntology_url":"https://syntology.ai/paper/2312.16341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16341"}},"official":{"repos":["shengroup/fedigw"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/user-consented-federated-recommender-system","slug":"user-consented-federated-recommender-system","title":"User Consented Federated Recommender System Against Personalized Attribute Inference Attack","date":"2023-12-23","arxiv_id":"2312.16203","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/user-consented-federated-recommender-system#ran","syntology_url":"https://syntology.ai/paper/2312.16203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16203"}},"official":{"repos":["hkust-knowcomp/uc-fedrec"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fed-co-2-cooperation-of-online-and-offline-1","slug":"fed-co-2-cooperation-of-online-and-offline-1","title":"Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning","date":"2023-12-21","arxiv_id":"2312.13923","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fed-co-2-cooperation-of-online-and-offline-1#ran","syntology_url":"https://syntology.ai/paper/2312.13923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13923"}},"official":{"repos":["zhyczy/fed-co2"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-with-extremely-noisy","slug":"federated-learning-with-extremely-noisy","title":"Federated Learning with Extremely Noisy Clients via Negative Distillation","date":"2023-12-20","arxiv_id":"2312.12703","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":17,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/federated-learning-with-extremely-noisy#ran","syntology_url":"https://syntology.ai/paper/2312.12703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12703"}},"official":{"repos":["linchen99/fedned"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/calibrated-one-round-federated-learning-with","slug":"calibrated-one-round-federated-learning-with","title":"Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space","date":"2023-12-15","arxiv_id":"2312.09817","repositories_listed":2,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":13,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/calibrated-one-round-federated-learning-with#ran","syntology_url":"https://syntology.ai/paper/2312.09817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09817"}},"official":{"repos":["hasanmohsin/betapredbayes_fl","hasanmohsin/betapredbayesfl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/exploiting-label-skews-in-federated-learning","slug":"exploiting-label-skews-in-federated-learning","title":"Exploiting Label Skews in Federated Learning with Model Concatenation","date":"2023-12-11","arxiv_id":"2312.06290","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/exploiting-label-skews-in-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2312.06290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06290"}},"official":{"repos":["sjtudyq/fedconcat"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-full-parameter-tuning-of-billion","slug":"federated-full-parameter-tuning-of-billion","title":"Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes","date":"2023-12-11","arxiv_id":"2312.06353","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/federated-full-parameter-tuning-of-billion#ran","syntology_url":"https://syntology.ai/paper/2312.06353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06353"}},"official":{"repos":["alibaba/federatedscope"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]}}},{"url":"/paper/pfllib-personalized-federated-learning","slug":"pfllib-personalized-federated-learning","title":"PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark","date":"2023-12-08","arxiv_id":"2312.04992","repositories_listed":6,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pfllib-personalized-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2312.04992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04992"}},"official":{"repos":["TsingZ0/PFLlib","TsingZ0/HtFL"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/think-twice-before-selection-federated","slug":"think-twice-before-selection-federated","title":"Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts","date":"2023-12-05","arxiv_id":"2312.02567","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/think-twice-before-selection-federated#ran","syntology_url":"https://syntology.ai/paper/2312.02567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02567"}},"official":{"repos":["jiayichen815/feal"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unlocking-the-potential-of-federated-learning-1","slug":"unlocking-the-potential-of-federated-learning-1","title":"Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents","date":"2023-12-03","arxiv_id":"2312.01537","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unlocking-the-potential-of-federated-learning-1#ran","syntology_url":"https://syntology.ai/paper/2312.01537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01537"}},"official":{"repos":["feddg23/feddg-main"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/addressing-membership-inference-attack-in","slug":"addressing-membership-inference-attack-in","title":"Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning","date":"2023-11-29","arxiv_id":"2311.17750","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/addressing-membership-inference-attack-in#ran","syntology_url":"https://syntology.ai/paper/2311.17750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17750"}},"official":{"repos":["ellisalicante/ma-fl-mia","negedng/ma-fl-mia"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/eliminating-domain-bias-for-federated","slug":"eliminating-domain-bias-for-federated","title":"Eliminating Domain Bias for Federated Learning in Representation Space","date":"2023-11-25","arxiv_id":"2311.14975","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":2,"n_ran_checked":7,"n_instrument":5,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"12 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/eliminating-domain-bias-for-federated#ran","syntology_url":"https://syntology.ai/paper/2311.14975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14975"}},"official":{"repos":["tsingz0/dbe"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/fedfn-feature-normalization-for-alleviating","slug":"fedfn-feature-normalization-for-alleviating","title":"FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning","date":"2023-11-22","arxiv_id":"2311.13267","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedfn-feature-normalization-for-alleviating#ran","syntology_url":"https://syntology.ai/paper/2311.13267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13267"}},"official":null}},{"url":"/paper/fedra-a-random-allocation-strategy-for","slug":"fedra-a-random-allocation-strategy-for","title":"FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients","date":"2023-11-19","arxiv_id":"2311.11227","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedra-a-random-allocation-strategy-for#ran","syntology_url":"https://syntology.ai/paper/2311.11227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11227"}},"official":{"repos":["leondada/fedra"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-for-generalization","slug":"federated-learning-for-generalization","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","date":"2023-11-12","arxiv_id":"2311.06750","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-learning-for-generalization#ran","syntology_url":"https://syntology.ai/paper/2311.06750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.06750"}},"official":{"repos":["wenkehuang/marsfl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scale-mia-a-scalable-model-inversion-attack","slug":"scale-mia-a-scalable-model-inversion-attack","title":"Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction","date":"2023-11-10","arxiv_id":"2311.05808","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scale-mia-a-scalable-model-inversion-attack#ran","syntology_url":"https://syntology.ai/paper/2311.05808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05808"}},"official":{"repos":["unknown123489/scale-mia"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-online-federated-learning-with","slug":"personalized-online-federated-learning-with","title":"Personalized Online Federated Learning with Multiple Kernels","date":"2023-11-09","arxiv_id":"2311.05108","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/personalized-online-federated-learning-with#ran","syntology_url":"https://syntology.ai/paper/2311.05108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05108"}},"official":{"repos":["pouyamghari/pof-mkl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/convergence-analysis-of-sequential-federated-1","slug":"convergence-analysis-of-sequential-federated-1","title":"Convergence Analysis of Sequential Federated Learning on Heterogeneous Data","date":"2023-11-06","arxiv_id":"2311.03154","repositories_listed":3,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/convergence-analysis-of-sequential-federated-1#ran","syntology_url":"https://syntology.ai/paper/2311.03154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03154"}},"official":{"repos":["bird-two/convergence","liyipeng00/convergence"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/adaptive-test-time-personalization-for-1","slug":"adaptive-test-time-personalization-for-1","title":"Adaptive Test-Time Personalization for Federated Learning","date":"2023-10-28","arxiv_id":"2310.18816","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":2,"n_ran_checked":4,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-test-time-personalization-for-1#ran","syntology_url":"https://syntology.ai/paper/2310.18816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18816"}},"official":{"repos":["baowenxuan/atp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/heterogeneous-federated-learning-with-group","slug":"heterogeneous-federated-learning-with-group","title":"Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning","date":"2023-10-27","arxiv_id":"2310.18285","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/heterogeneous-federated-learning-with-group#ran","syntology_url":"https://syntology.ai/paper/2310.18285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18285"}},"official":null}},{"url":"/paper/navigating-data-heterogeneity-in-federated","slug":"navigating-data-heterogeneity-in-federated","title":"Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection","date":"2023-10-26","arxiv_id":"2310.17097","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/navigating-data-heterogeneity-in-federated#ran","syntology_url":"https://syntology.ai/paper/2310.17097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17097"}},"official":{"repos":["Kthyeon/ssfod"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-of-large-language-models","slug":"federated-learning-of-large-language-models","title":"Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization","date":"2023-10-23","arxiv_id":"2310.15080","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/federated-learning-of-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2310.15080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15080"}},"official":{"repos":["llm-eff/fedpeptao"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/competitive-advantage-attacks-to","slug":"competitive-advantage-attacks-to","title":"Competitive Advantage Attacks to Decentralized Federated Learning","date":"2023-10-20","arxiv_id":"2310.13862","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/competitive-advantage-attacks-to#ran","syntology_url":"https://syntology.ai/paper/2310.13862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13862"}},"official":null}},{"url":"/paper/fate-llm-a-industrial-grade-federated","slug":"fate-llm-a-industrial-grade-federated","title":"FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models","date":"2023-10-16","arxiv_id":"2310.10049","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fate-llm-a-industrial-grade-federated#ran","syntology_url":"https://syntology.ai/paper/2310.10049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10049"}},"official":{"repos":["FederatedAI/FATE-LLM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/flrce-efficient-federated-learning-with","slug":"flrce-efficient-federated-learning-with","title":"FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy","date":"2023-10-15","arxiv_id":"2310.09789","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/flrce-efficient-federated-learning-with#ran","syntology_url":"https://syntology.ai/paper/2310.09789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09789"}},"official":{"repos":["ziruniu0/flrce"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/prior-personalized-prior-for-reactivating-the","slug":"prior-personalized-prior-for-reactivating-the","title":"PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning","date":"2023-10-13","arxiv_id":"2310.09183","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/prior-personalized-prior-for-reactivating-the#ran","syntology_url":"https://syntology.ai/paper/2310.09183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09183"}},"official":{"repos":["bdemo/pfedbred_public"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/find-your-optimal-assignments-on-the-fly-a","slug":"find-your-optimal-assignments-on-the-fly-a","title":"Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning","date":"2023-10-09","arxiv_id":"2310.05397","repositories_listed":0,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":12,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/find-your-optimal-assignments-on-the-fly-a#ran","syntology_url":"https://syntology.ai/paper/2310.05397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05397"}},"official":null}},{"url":"/paper/fedfed-feature-distillation-against-data-1","slug":"fedfed-feature-distillation-against-data-1","title":"FedFed: Feature Distillation against Data Heterogeneity in Federated Learning","date":"2023-10-08","arxiv_id":"2310.05077","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fedfed-feature-distillation-against-data-1#ran","syntology_url":"https://syntology.ai/paper/2310.05077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05077"}},"official":{"repos":["visitworld123/fedfed"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedconv-enhancing-convolutional-neural","slug":"fedconv-enhancing-convolutional-neural","title":"FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning","date":"2023-10-06","arxiv_id":"2310.04412","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fedconv-enhancing-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/2310.04412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04412"}},"official":{"repos":["ucsc-vlaa/fedconv"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-with-differential-privacy","slug":"federated-learning-with-differential-privacy","title":"Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping","date":"2023-09-29","arxiv_id":"2310.00098","repositories_listed":0,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/federated-learning-with-differential-privacy#ran","syntology_url":"https://syntology.ai/paper/2310.00098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00098"}},"official":null}},{"url":"/paper/fedaiot-a-federated-learning-benchmark-for","slug":"fedaiot-a-federated-learning-benchmark-for","title":"FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things","date":"2023-09-29","arxiv_id":"2310.00109","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedaiot-a-federated-learning-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2310.00109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00109"}},"official":{"repos":["aiot-mlsys-lab/fedaiot"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generalizable-heterogeneous-federated-cross","slug":"generalizable-heterogeneous-federated-cross","title":"Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning","date":"2023-09-28","arxiv_id":"2309.16286","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generalizable-heterogeneous-federated-cross#ran","syntology_url":"https://syntology.ai/paper/2309.16286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16286"}},"official":{"repos":["wenkehuang/fccl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedcompass-efficient-cross-silo-federated","slug":"fedcompass-efficient-cross-silo-federated","title":"FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler","date":"2023-09-26","arxiv_id":"2309.14675","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fedcompass-efficient-cross-silo-federated#ran","syntology_url":"https://syntology.ai/paper/2309.14675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14675"}},"official":{"repos":["appfl/fedcompass"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bold-but-cautious-unlocking-the-potential-of","slug":"bold-but-cautious-unlocking-the-potential-of","title":"Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration","date":"2023-09-20","arxiv_id":"2309.11103","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bold-but-cautious-unlocking-the-potential-of#ran","syntology_url":"https://syntology.ai/paper/2309.11103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11103"}},"official":{"repos":["kxzxvbk/Fling"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mitigating-adversarial-attacks-in-federated","slug":"mitigating-adversarial-attacks-in-federated","title":"Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments","date":"2023-09-13","arxiv_id":"2309.07197","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mitigating-adversarial-attacks-in-federated#ran","syntology_url":"https://syntology.ai/paper/2309.07197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07197"}},"official":{"repos":["queyrusi/pelta"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/bias-propagation-in-federated-learning","slug":"bias-propagation-in-federated-learning","title":"Bias Propagation in Federated Learning","date":"2023-09-05","arxiv_id":"2309.02160","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bias-propagation-in-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2309.02160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.02160"}},"official":{"repos":["privacytrustlab/bias_in_FL"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cefhri-a-communication-efficient-federated","slug":"cefhri-a-communication-efficient-federated","title":"CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction","date":"2023-08-29","arxiv_id":"2308.14965","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cefhri-a-communication-efficient-federated#ran","syntology_url":"https://syntology.ai/paper/2308.14965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14965"}},"official":{"repos":["umarkhalidai/cefhri-efficient-federated-learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-fine-tuning-of-billion-sized","slug":"federated-fine-tuning-of-billion-sized","title":"FwdLLM: Efficient FedLLM using Forward Gradient","date":"2023-08-26","arxiv_id":"2308.13894","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/federated-fine-tuning-of-billion-sized#ran","syntology_url":"https://syntology.ai/paper/2308.13894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13894"}},"official":{"repos":["ubiquitouslearning/fwdllm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedsol-bridging-global-alignment-and-local","slug":"fedsol-bridging-global-alignment-and-local","title":"FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning","date":"2023-08-24","arxiv_id":"2308.12532","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedsol-bridging-global-alignment-and-local#ran","syntology_url":"https://syntology.ai/paper/2308.12532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12532"}},"official":{"repos":["Lee-Gihun/FedSOL"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lr-xfl-logical-reasoning-based-explainable","slug":"lr-xfl-logical-reasoning-based-explainable","title":"LR-XFL: Logical Reasoning-based Explainable Federated Learning","date":"2023-08-24","arxiv_id":"2308.12681","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/lr-xfl-logical-reasoning-based-explainable#ran","syntology_url":"https://syntology.ai/paper/2308.12681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12681"}},"official":{"repos":["yanci87/lr-xfl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-of-causal-effects-from","slug":"federated-learning-of-causal-effects-from","title":"Federated Causal Inference from Observational Data","date":"2023-08-24","arxiv_id":"2308.13047","repositories_listed":3,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/federated-learning-of-causal-effects-from#ran","syntology_url":"https://syntology.ai/paper/2308.13047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13047"}},"official":{"repos":["vothanhvinh/causalfi","vothanhvinh/causalrff","vothanhvinh/fedci"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/internal-cross-layer-gradients-for-extending","slug":"internal-cross-layer-gradients-for-extending","title":"Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning","date":"2023-08-22","arxiv_id":"2308.11464","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/internal-cross-layer-gradients-for-extending#ran","syntology_url":"https://syntology.ai/paper/2308.11464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11464"}},"official":{"repos":["chanyunhin/inco-aggregation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-hessian-alignment-for-domain","slug":"understanding-hessian-alignment-for-domain","title":"Understanding Hessian Alignment for Domain Generalization","date":"2023-08-22","arxiv_id":"2308.11778","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/understanding-hessian-alignment-for-domain#ran","syntology_url":"https://syntology.ai/paper/2308.11778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11778"}},"official":{"repos":["huawei-noah/federated-learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/feddat-an-approach-for-foundation-model","slug":"feddat-an-approach-for-foundation-model","title":"FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning","date":"2023-08-21","arxiv_id":"2308.12305","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/feddat-an-approach-for-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2308.12305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12305"}},"official":{"repos":["HaokunChen245/FedDAT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gpfl-simultaneously-learning-global-and","slug":"gpfl-simultaneously-learning-global-and","title":"GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning","date":"2023-08-20","arxiv_id":"2308.10279","repositories_listed":4,"syntology":{"n":17,"n_ran":9,"n_constructed":1,"n_ran_checked":3,"n_instrument":6,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/gpfl-simultaneously-learning-global-and#ran","syntology_url":"https://syntology.ai/paper/2308.10279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10279"}},"official":{"repos":["TsingZ0/GPFL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/towards-attack-tolerant-federated-learning","slug":"towards-attack-tolerant-federated-learning","title":"Towards Attack-tolerant Federated Learning via Critical Parameter Analysis","date":"2023-08-18","arxiv_id":"2308.09318","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-attack-tolerant-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2308.09318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09318"}},"official":{"repos":["sungwon-han/fedcpa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fedperfix-towards-partial-model","slug":"fedperfix-towards-partial-model","title":"FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning","date":"2023-08-17","arxiv_id":"2308.09160","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":4,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fedperfix-towards-partial-model#ran","syntology_url":"https://syntology.ai/paper/2308.09160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09160"}},"official":{"repos":["imguangyu/fedperfix"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gifd-a-generative-gradient-inversion-method","slug":"gifd-a-generative-gradient-inversion-method","title":"GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization","date":"2023-08-09","arxiv_id":"2308.04699","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gifd-a-generative-gradient-inversion-method#ran","syntology_url":"https://syntology.ai/paper/2308.04699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04699"}},"official":{"repos":["ffhibnese/gifd"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-zeroth-order-optimization-using","slug":"federated-zeroth-order-optimization-using","title":"Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients","date":"2023-08-08","arxiv_id":"2308.04077","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-zeroth-order-optimization-using#ran","syntology_url":"https://syntology.ai/paper/2308.04077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04077"}},"official":{"repos":["shuyao95/FZooS"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/serverless-federated-auprc-optimization-for","slug":"serverless-federated-auprc-optimization-for","title":"Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining","date":"2023-08-06","arxiv_id":"2308.03035","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/serverless-federated-auprc-optimization-for#ran","syntology_url":"https://syntology.ai/paper/2308.03035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03035"}},"official":{"repos":["xidongwu/d-auprc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/you-can-backdoor-personalized-federated","slug":"you-can-backdoor-personalized-federated","title":"You Can Backdoor Personalized Federated Learning","date":"2023-07-29","arxiv_id":"2307.15971","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/you-can-backdoor-personalized-federated#ran","syntology_url":"https://syntology.ai/paper/2307.15971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.15971"}},"official":{"repos":["bapfl/code"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-model-aggregation-via-self","slug":"federated-model-aggregation-via-self","title":"Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification","date":"2023-07-27","arxiv_id":"2307.14959","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":13,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/federated-model-aggregation-via-self#ran","syntology_url":"https://syntology.ai/paper/2307.14959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14959"}},"official":{"repos":["xmed-lab/fed-mas"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/demystifying-local-and-global-fairness-trade","slug":"demystifying-local-and-global-fairness-trade","title":"Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition","date":"2023-07-21","arxiv_id":"2307.11333","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/demystifying-local-and-global-fairness-trade#ran","syntology_url":"https://syntology.ai/paper/2307.11333","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11333"}},"official":{"repos":["faisalhamman/fairfl-pid"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-federated-foundation-models-scalable-1","slug":"towards-federated-foundation-models-scalable-1","title":"Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning","date":"2023-07-18","arxiv_id":"2307.09619","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-federated-foundation-models-scalable-1#ran","syntology_url":"https://syntology.ai/paper/2307.09619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09619"}},"official":{"repos":["google-research/dataset_grouper"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/l-dawa-layer-wise-divergence-aware-weight","slug":"l-dawa-layer-wise-divergence-aware-weight","title":"L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning","date":"2023-07-14","arxiv_id":"2307.07393","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/l-dawa-layer-wise-divergence-aware-weight#ran","syntology_url":"https://syntology.ai/paper/2307.07393","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07393"}},"official":{"repos":["yasar-rehman/L-DAWA"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/layerwise-linear-mode-connectivity","slug":"layerwise-linear-mode-connectivity","title":"Layer-wise Linear Mode Connectivity","date":"2023-07-13","arxiv_id":"2307.06966","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":18,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/layerwise-linear-mode-connectivity#ran","syntology_url":"https://syntology.ai/paper/2307.06966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06966"}},"official":{"repos":["link-er/layer-wise-lmc"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/locally-adaptive-federated-learning-via","slug":"locally-adaptive-federated-learning-via","title":"Locally Adaptive Federated Learning","date":"2023-07-12","arxiv_id":"2307.06306","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/locally-adaptive-federated-learning-via#ran","syntology_url":"https://syntology.ai/paper/2307.06306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06306"}},"official":{"repos":["IssamLaradji/sps"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fdapt-federated-domain-adaptive-pre-training","slug":"fdapt-federated-domain-adaptive-pre-training","title":"FDAPT: Federated Domain-adaptive Pre-training for Language Models","date":"2023-07-12","arxiv_id":"2307.06933","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fdapt-federated-domain-adaptive-pre-training#ran","syntology_url":"https://syntology.ai/paper/2307.06933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06933"}},"official":{"repos":["scylj1/FDAPT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-algorithms-for-federated-domain","slug":"benchmarking-algorithms-for-federated-domain","title":"Benchmarking Algorithms for Federated Domain Generalization","date":"2023-07-11","arxiv_id":"2307.04942","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/benchmarking-algorithms-for-federated-domain#ran","syntology_url":"https://syntology.ai/paper/2307.04942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.04942"}},"official":{"repos":["inouye-lab/feddg_benchmark"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fluid-mitigating-stragglers-in-federated-1","slug":"fluid-mitigating-stragglers-in-federated-1","title":"FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout","date":"2023-07-05","arxiv_id":"2307.02623","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fluid-mitigating-stragglers-in-federated-1#ran","syntology_url":"https://syntology.ai/paper/2307.02623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02623"}},"official":{"repos":["iwang05/fluid"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedcp-separating-feature-information-for","slug":"fedcp-separating-feature-information-for","title":"FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy","date":"2023-07-01","arxiv_id":"2307.01217","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fedcp-separating-feature-information-for#ran","syntology_url":"https://syntology.ai/paper/2307.01217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01217"}},"official":{"repos":["tsingz0/fedcp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedsampling-a-better-sampling-strategy-for","slug":"fedsampling-a-better-sampling-strategy-for","title":"FedSampling: A Better Sampling Strategy for Federated Learning","date":"2023-06-25","arxiv_id":"2306.14245","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedsampling-a-better-sampling-strategy-for#ran","syntology_url":"https://syntology.ai/paper/2306.14245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14245"}},"official":null}},{"url":"/paper/communication-efficient-federated-learning-20","slug":"communication-efficient-federated-learning-20","title":"Adaptive Compression in Federated Learning via Side Information","date":"2023-06-22","arxiv_id":"2306.12625","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/communication-efficient-federated-learning-20#ran","syntology_url":"https://syntology.ai/paper/2306.12625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12625"}},"official":{"repos":["francescopase/federated-klms"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-federated-learning-with-feature","slug":"personalized-federated-learning-with-feature","title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration","date":"2023-06-20","arxiv_id":"2306.11867","repositories_listed":4,"syntology":{"n":16,"n_ran":12,"n_constructed":5,"n_ran_checked":8,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"12 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/personalized-federated-learning-with-feature#ran","syntology_url":"https://syntology.ai/paper/2306.11867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11867"}},"official":{"repos":["jianxu95/fedpac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/federated-few-shot-learning","slug":"federated-few-shot-learning","title":"Federated Few-shot Learning","date":"2023-06-17","arxiv_id":"2306.10234","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/federated-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2306.10234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10234"}},"official":{"repos":["songw-sw/f2l"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimizing-the-collaboration-structure-in","slug":"optimizing-the-collaboration-structure-in","title":"Optimizing the Collaboration Structure in Cross-Silo Federated Learning","date":"2023-06-10","arxiv_id":"2306.06508","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":3,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/optimizing-the-collaboration-structure-in#ran","syntology_url":"https://syntology.ai/paper/2306.06508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06508"}},"official":{"repos":["baowenxuan/fedcollab"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-you-may-communicate-less","slug":"federated-learning-you-may-communicate-less","title":"Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!","date":"2023-06-09","arxiv_id":"2306.05862","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/federated-learning-you-may-communicate-less#ran","syntology_url":"https://syntology.ai/paper/2306.05862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05862"}},"official":{"repos":["romainchor/generalization_fl_icml2024"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/communication-efficient-gradient-descent","slug":"communication-efficient-gradient-descent","title":"Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates","date":"2023-06-08","arxiv_id":"2306.05100","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/communication-efficient-gradient-descent#ran","syntology_url":"https://syntology.ai/paper/2306.05100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05100"}},"official":{"repos":["isayantan/proxskipvip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-optimal-locally-private-mean-estimation-1","slug":"fast-optimal-locally-private-mean-estimation-1","title":"Fast Optimal Locally Private Mean Estimation via Random Projections","date":"2023-06-07","arxiv_id":"2306.04444","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-optimal-locally-private-mean-estimation-1#ran","syntology_url":"https://syntology.ai/paper/2306.04444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04444"}},"official":{"repos":["apple/ml-projunit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gpt-fl-generative-pre-trained-model-assisted","slug":"gpt-fl-generative-pre-trained-model-assisted","title":"GPT-FL: Generative Pre-trained Model-Assisted Federated Learning","date":"2023-06-03","arxiv_id":"2306.02210","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gpt-fl-generative-pre-trained-model-assisted#ran","syntology_url":"https://syntology.ai/paper/2306.02210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02210"}},"official":{"repos":["AvestimehrResearchGroup/GPT-FL"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/forgettable-federated-linear-learning-with","slug":"forgettable-federated-linear-learning-with","title":"Forgettable Federated Linear Learning with Certified Data Unlearning","date":"2023-06-03","arxiv_id":"2306.02216","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/forgettable-federated-linear-learning-with#ran","syntology_url":"https://syntology.ai/paper/2306.02216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02216"}},"official":{"repos":["nanboy-ronan/2f2l-federated-unlearning"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/surrogate-model-extension-sme-a-fast-and","slug":"surrogate-model-extension-sme-a-fast-and","title":"Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning","date":"2023-05-31","arxiv_id":"2306.00127","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/surrogate-model-extension-sme-a-fast-and#ran","syntology_url":"https://syntology.ai/paper/2306.00127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00127"}},"official":{"repos":["junyizhu-ai/surrogate_model_extension"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/feddisco-federated-learning-with-discrepancy","slug":"feddisco-federated-learning-with-discrepancy","title":"FedDisco: Federated Learning with Discrepancy-Aware Collaboration","date":"2023-05-30","arxiv_id":"2305.19229","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/feddisco-federated-learning-with-discrepancy#ran","syntology_url":"https://syntology.ai/paper/2305.19229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19229"}},"official":{"repos":["mediabrain-sjtu/feddisco"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/parameter-efficient-fine-tuning-without","slug":"parameter-efficient-fine-tuning-without","title":"Parameter-Efficient Fine-Tuning without Introducing New Latency","date":"2023-05-26","arxiv_id":"2305.16742","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/parameter-efficient-fine-tuning-without#ran","syntology_url":"https://syntology.ai/paper/2305.16742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16742"}},"official":null}},{"url":"/paper/fedzero-leveraging-renewable-excess-energy-in","slug":"fedzero-leveraging-renewable-excess-energy-in","title":"FedZero: Leveraging Renewable Excess Energy in Federated Learning","date":"2023-05-24","arxiv_id":"2305.15092","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fedzero-leveraging-renewable-excess-energy-in#ran","syntology_url":"https://syntology.ai/paper/2305.15092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15092"}},"official":{"repos":["dos-group/fedzero"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/explicit-personalization-and-local-training","slug":"explicit-personalization-and-local-training","title":"Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning","date":"2023-05-22","arxiv_id":"2305.13170","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/explicit-personalization-and-local-training#ran","syntology_url":"https://syntology.ai/paper/2305.13170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13170"}},"official":{"repos":["williamyi96/scafflix"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/xtab-cross-table-pretraining-for-tabular","slug":"xtab-cross-table-pretraining-for-tabular","title":"XTab: Cross-table Pretraining for Tabular Transformers","date":"2023-05-10","arxiv_id":"2305.06090","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/xtab-cross-table-pretraining-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2305.06090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06090"}},"official":{"repos":["bingzhaozhu/xtab"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fednoro-towards-noise-robust-federated","slug":"fednoro-towards-noise-robust-federated","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","date":"2023-05-09","arxiv_id":"2305.05230","repositories_listed":3,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fednoro-towards-noise-robust-federated#ran","syntology_url":"https://syntology.ai/paper/2305.05230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05230"}},"official":{"repos":["wnn2000/fednoro"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-building-the-federated-gpt-federated","slug":"towards-building-the-federated-gpt-federated","title":"Towards Building the Federated GPT: Federated Instruction Tuning","date":"2023-05-09","arxiv_id":"2305.05644","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-building-the-federated-gpt-federated#ran","syntology_url":"https://syntology.ai/paper/2305.05644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05644"}},"official":{"repos":["jayzhang42/federatedgpt-shepherd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-personalized-federated-learning-via","slug":"efficient-personalized-federated-learning-via","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","date":"2023-05-04","arxiv_id":"2305.02776","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":2,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-personalized-federated-learning-via#ran","syntology_url":"https://syntology.ai/paper/2305.02776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02776"}},"official":{"repos":["alibaba/federatedscope","yxdyc/pfedgate"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-federated-learning-under-mixture","slug":"personalized-federated-learning-under-mixture","title":"Personalized Federated Learning under Mixture of Distributions","date":"2023-05-01","arxiv_id":"2305.01068","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/personalized-federated-learning-under-mixture#ran","syntology_url":"https://syntology.ai/paper/2305.01068","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.01068"}},"official":{"repos":["zshuai8/FedGMM_ICML2023"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/chameleon-adapting-to-peer-images-for","slug":"chameleon-adapting-to-peer-images-for","title":"Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning","date":"2023-04-25","arxiv_id":"2304.12961","repositories_listed":1,"syntology":{"n":11,"n_ran":5,"n_constructed":4,"n_ran_checked":5,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/chameleon-adapting-to-peer-images-for#ran","syntology_url":"https://syntology.ai/paper/2304.12961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.12961"}},"official":{"repos":["ybdai7/Chameleon-durable-backdoor"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-incremental-semantic-segmentation","slug":"federated-incremental-semantic-segmentation","title":"Federated Incremental Semantic Segmentation","date":"2023-04-10","arxiv_id":"2304.04620","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/federated-incremental-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2304.04620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04620"}},"official":{"repos":["jiahuadong/fiss"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/asynchronous-federated-continual-learning","slug":"asynchronous-federated-continual-learning","title":"Asynchronous Federated Continual Learning","date":"2023-04-07","arxiv_id":"2304.03626","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/asynchronous-federated-continual-learning#ran","syntology_url":"https://syntology.ai/paper/2304.03626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03626"}},"official":{"repos":["lttm/fedspace"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/selective-knowledge-sharing-for-privacy","slug":"selective-knowledge-sharing-for-privacy","title":"Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher","date":"2023-04-04","arxiv_id":"2304.01731","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/selective-knowledge-sharing-for-privacy#ran","syntology_url":"https://syntology.ai/paper/2304.01731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01731"}},"official":{"repos":["shaojiawei07/selective-fd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/re-thinking-federated-active-learning-based","slug":"re-thinking-federated-active-learning-based","title":"Re-thinking Federated Active Learning based on Inter-class Diversity","date":"2023-03-22","arxiv_id":"2303.12317","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/re-thinking-federated-active-learning-based#ran","syntology_url":"https://syntology.ai/paper/2303.12317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12317"}},"official":{"repos":["raymin0223/logo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedml-he-an-efficient-homomorphic-encryption","slug":"fedml-he-an-efficient-homomorphic-encryption","title":"FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System","date":"2023-03-20","arxiv_id":"2303.10837","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedml-he-an-efficient-homomorphic-encryption#ran","syntology_url":"https://syntology.ai/paper/2303.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10837"}},"official":{"repos":["FedML-AI/FedML"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/make-landscape-flatter-in-differentially","slug":"make-landscape-flatter-in-differentially","title":"Make Landscape Flatter in Differentially Private Federated Learning","date":"2023-03-20","arxiv_id":"2303.11242","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/make-landscape-flatter-in-differentially#ran","syntology_url":"https://syntology.ai/paper/2303.11242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11242"}},"official":{"repos":["YMJS-Irfan/DP-FedSAM"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/no-fear-of-classifier-biases-neural-collapse","slug":"no-fear-of-classifier-biases-neural-collapse","title":"No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier","date":"2023-03-17","arxiv_id":"2303.10058","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/no-fear-of-classifier-biases-neural-collapse#ran","syntology_url":"https://syntology.ai/paper/2303.10058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10058"}},"official":{"repos":["zexilee/iccv-2023-fedetf"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"cf81b10e681265860cd0164ef29d072c926084d2e2e8809507022bee743be202","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}