{"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/4","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":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"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/3","next":"/task/federated-learning/papers/ran/5","papers":[{"url":"/paper/rscfed-random-sampling-consensus-federated","slug":"rscfed-random-sampling-consensus-federated","title":"RSCFed: Random Sampling Consensus Federated Semi-supervised Learning","date":"2022-03-26","arxiv_id":"2203.13993","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"6 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/rscfed-random-sampling-consensus-federated#ran","syntology_url":"https://syntology.ai/paper/2203.13993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13993"}},"official":{"repos":["xmed-lab/rscfed"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/flute-a-scalable-extensible-framework-for","slug":"flute-a-scalable-extensible-framework-for","title":"FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations","date":"2022-03-25","arxiv_id":"2203.13789","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":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) · 1 unverified","sample_list":"/paper/flute-a-scalable-extensible-framework-for#ran","syntology_url":"https://syntology.ai/paper/2203.13789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13789"}},"official":{"repos":["microsoft/msrflute"],"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/federated-class-incremental-learning","slug":"federated-class-incremental-learning","title":"Federated Class-Incremental Learning","date":"2022-03-22","arxiv_id":"2203.11473","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-class-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/2203.11473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11473"}},"official":{"repos":["conditionwang/fcil"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/improving-generalization-in-federated","slug":"improving-generalization-in-federated","title":"Improving Generalization in Federated Learning by Seeking Flat Minima","date":"2022-03-22","arxiv_id":"2203.11834","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/improving-generalization-in-federated#ran","syntology_url":"https://syntology.ai/paper/2203.11834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11834"}},"official":{"repos":["debcaldarola/fedsam"],"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/efficient-split-mix-federated-learning-for-on-1","slug":"efficient-split-mix-federated-learning-for-on-1","title":"Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization","date":"2022-03-18","arxiv_id":"2203.09747","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-split-mix-federated-learning-for-on-1#ran","syntology_url":"https://syntology.ai/paper/2203.09747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09747"}},"official":{"repos":["illidanlab/SplitMix"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mpaf-model-poisoning-attacks-to-federated","slug":"mpaf-model-poisoning-attacks-to-federated","title":"MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients","date":"2022-03-16","arxiv_id":"2203.08669","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/mpaf-model-poisoning-attacks-to-federated#ran","syntology_url":"https://syntology.ai/paper/2203.08669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08669"}},"official":null}},{"url":"/paper/energy-latency-attacks-via-sponge-poisoning","slug":"energy-latency-attacks-via-sponge-poisoning","title":"Energy-Latency Attacks via Sponge Poisoning","date":"2022-03-14","arxiv_id":"2203.08147","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/energy-latency-attacks-via-sponge-poisoning#ran","syntology_url":"https://syntology.ai/paper/2203.08147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08147"}},"official":{"repos":["cinofix/sponge_poisoning_energy_latency_attack","iliaishacked/sponge_examples"],"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/coco-fl-communication-and-computation-aware","slug":"coco-fl-communication-and-computation-aware","title":"CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization","date":"2022-03-10","arxiv_id":"2203.05468","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/coco-fl-communication-and-computation-aware#ran","syntology_url":"https://syntology.ai/paper/2203.05468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05468"}},"official":{"repos":["k1l1/cocofl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/feddrive-generalizing-federated-learning-to","slug":"feddrive-generalizing-federated-learning-to","title":"FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving","date":"2022-02-28","arxiv_id":"2202.13670","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/feddrive-generalizing-federated-learning-to#ran","syntology_url":"https://syntology.ai/paper/2202.13670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13670"}},"official":{"repos":["Erosinho13/FedDrive"],"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/lamp-extracting-text-from-gradients-with","slug":"lamp-extracting-text-from-gradients-with","title":"LAMP: Extracting Text from Gradients with Language Model Priors","date":"2022-02-17","arxiv_id":"2202.08827","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/lamp-extracting-text-from-gradients-with#ran","syntology_url":"https://syntology.ai/paper/2202.08827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.08827"}},"official":{"repos":["eth-sri/lamp"],"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/on-the-convergence-of-clustered-federated","slug":"on-the-convergence-of-clustered-federated","title":"On the Convergence of Clustered Federated Learning","date":"2022-02-13","arxiv_id":"2202.06187","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-the-convergence-of-clustered-federated#ran","syntology_url":"https://syntology.ai/paper/2202.06187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.06187"}},"official":{"repos":["jie-ma-ai/FedBase"],"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/byzantine-robust-decentralized-learning-via","slug":"byzantine-robust-decentralized-learning-via","title":"Byzantine-Robust Decentralized Learning via ClippedGossip","date":"2022-02-03","arxiv_id":"2202.01545","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/byzantine-robust-decentralized-learning-via#ran","syntology_url":"https://syntology.ai/paper/2202.01545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.01545"}},"official":{"repos":["epfml/byzantine-robust-decentralized-optimizer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/recycling-model-updates-in-federated-learning-1","slug":"recycling-model-updates-in-federated-learning-1","title":"Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?","date":"2022-02-01","arxiv_id":"2202.00280","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":2,"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/recycling-model-updates-in-federated-learning-1#ran","syntology_url":"https://syntology.ai/paper/2202.00280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.00280"}},"official":{"repos":["shams-sam/fedoptim"],"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"]}}},{"url":"/paper/fedgcn-convergence-and-communication","slug":"fedgcn-convergence-and-communication","title":"FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks","date":"2022-01-28","arxiv_id":"2201.12433","repositories_listed":2,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/fedgcn-convergence-and-communication#ran","syntology_url":"https://syntology.ai/paper/2201.12433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12433"}},"official":{"repos":["yh-yao/FedGCN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/a-practical-data-free-approach-to-one-shot","slug":"a-practical-data-free-approach-to-one-shot","title":"DENSE: Data-Free One-Shot Federated Learning","date":"2021-12-23","arxiv_id":"2112.12371","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":9,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-practical-data-free-approach-to-one-shot#ran","syntology_url":"https://syntology.ai/paper/2112.12371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.12371"}},"official":{"repos":["zj-jayzhang/DENSE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/harmofl-harmonizing-local-and-global-drifts","slug":"harmofl-harmonizing-local-and-global-drifts","title":"HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images","date":"2021-12-20","arxiv_id":"2112.10775","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/harmofl-harmonizing-local-and-global-drifts#ran","syntology_url":"https://syntology.ai/paper/2112.10775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10775"}},"official":{"repos":["med-air/harmofl"],"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/federated-dynamic-sparse-training-computing","slug":"federated-dynamic-sparse-training-computing","title":"Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better","date":"2021-12-18","arxiv_id":"2112.09824","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/federated-dynamic-sparse-training-computing#ran","syntology_url":"https://syntology.ai/paper/2112.09824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.09824"}},"official":{"repos":["bibikar/feddst"],"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"]}}},{"url":"/paper/sparsefed-mitigating-model-poisoning-attacks","slug":"sparsefed-mitigating-model-poisoning-attacks","title":"SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification","date":"2021-12-12","arxiv_id":"2112.06274","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/sparsefed-mitigating-model-poisoning-attacks#ran","syntology_url":"https://syntology.ai/paper/2112.06274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.06274"}},"official":{"repos":["sparsefed/sparsefed"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-gradient-inversion-attacks-and-1","slug":"evaluating-gradient-inversion-attacks-and-1","title":"Evaluating Gradient Inversion Attacks and Defenses in Federated Learning","date":"2021-11-30","arxiv_id":"2112.00059","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 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; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/evaluating-gradient-inversion-attacks-and-1#ran","syntology_url":"https://syntology.ai/paper/2112.00059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.00059"}},"official":{"repos":["Princeton-SysML/GradAttack"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/stfl-a-temporal-spatial-federated-learning","slug":"stfl-a-temporal-spatial-federated-learning","title":"STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks","date":"2021-11-12","arxiv_id":"2111.06750","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/stfl-a-temporal-spatial-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2111.06750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06750"}},"official":{"repos":["jw9msjwjnpdrlfw/tsfl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-based-on-dynamic-1","slug":"federated-learning-based-on-dynamic-1","title":"Federated Learning Based on Dynamic Regularization","date":"2021-11-08","arxiv_id":"2111.04263","repositories_listed":5,"syntology":{"n":15,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"6 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; 1 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/federated-learning-based-on-dynamic-1#ran","syntology_url":"https://syntology.ai/paper/2111.04263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.04263"}},"official":{"repos":["alpemreacar/FedDyn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/bayesian-framework-for-gradient-leakage-1","slug":"bayesian-framework-for-gradient-leakage-1","title":"Bayesian Framework for Gradient Leakage","date":"2021-11-08","arxiv_id":"2111.04706","repositories_listed":2,"syntology":{"n":15,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 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; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/bayesian-framework-for-gradient-leakage-1#ran","syntology_url":"https://syntology.ai/paper/2111.04706","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.04706"}},"official":{"repos":["eth-sri/bayes-framework-leakage"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/parameterized-knowledge-transfer-for","slug":"parameterized-knowledge-transfer-for","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","date":"2021-11-04","arxiv_id":"2111.02862","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/parameterized-knowledge-transfer-for#ran","syntology_url":"https://syntology.ai/paper/2111.02862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02862"}},"official":null}},{"url":"/paper/efficient-passive-membership-inference-attack","slug":"efficient-passive-membership-inference-attack","title":"Efficient passive membership inference attack in federated learning","date":"2021-10-31","arxiv_id":"2111.00430","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-passive-membership-inference-attack#ran","syntology_url":"https://syntology.ai/paper/2111.00430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00430"}},"official":{"repos":["spin-umass/membershipwhiteboxattacks"],"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":["official"]}}},{"url":"/paper/gradient-inversion-with-generative-image","slug":"gradient-inversion-with-generative-image","title":"Gradient Inversion with Generative Image Prior","date":"2021-10-28","arxiv_id":"2110.14962","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/gradient-inversion-with-generative-image#ran","syntology_url":"https://syntology.ai/paper/2110.14962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14962"}},"official":{"repos":["ml-postech/gradient-inversion-generative-image-prior"],"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/qu-anti-zation-exploiting-quantization","slug":"qu-anti-zation-exploiting-quantization","title":"Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes","date":"2021-10-26","arxiv_id":"2110.13541","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":10,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 10 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 10 samples that ran constructed an object rather than computing a result","sample_list":"/paper/qu-anti-zation-exploiting-quantization#ran","syntology_url":"https://syntology.ai/paper/2110.13541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13541"}},"official":{"repos":["secure-ai-systems-group/qu-anti-zation"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fl-wbc-enhancing-robustness-against-model","slug":"fl-wbc-enhancing-robustness-against-model","title":"FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective","date":"2021-10-26","arxiv_id":"2110.13864","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fl-wbc-enhancing-robustness-against-model#ran","syntology_url":"https://syntology.ai/paper/2110.13864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13864"}},"official":{"repos":["jeremy313/fl-wbc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fault-tolerant-federated-reinforcement","slug":"fault-tolerant-federated-reinforcement","title":"Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee","date":"2021-10-26","arxiv_id":"2110.14074","repositories_listed":2,"syntology":{"n":15,"n_ran":9,"n_constructed":3,"n_ran_checked":4,"n_instrument":5,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":15,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/fault-tolerant-federated-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2110.14074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14074"}},"official":{"repos":["flint-xf-fan/Byzantine-Federeated-RL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/cafe-catastrophic-data-leakage-in-vertical","slug":"cafe-catastrophic-data-leakage-in-vertical","title":"CAFE: Catastrophic Data Leakage in Vertical Federated Learning","date":"2021-10-26","arxiv_id":"2110.15122","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/cafe-catastrophic-data-leakage-in-vertical#ran","syntology_url":"https://syntology.ai/paper/2110.15122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15122"}},"official":{"repos":["derafael/cafe"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fedhe-heterogeneous-models-and-communication","slug":"fedhe-heterogeneous-models-and-communication","title":"FedHe: Heterogeneous Models and Communication-Efficient Federated Learning","date":"2021-10-19","arxiv_id":"2110.09910","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/fedhe-heterogeneous-models-and-communication#ran","syntology_url":"https://syntology.ai/paper/2110.09910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09910"}},"official":{"repos":["ChanYunHin/FedHe"],"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/towards-federated-bayesian-network-structure","slug":"towards-federated-bayesian-network-structure","title":"Towards Federated Bayesian Network Structure Learning with Continuous Optimization","date":"2021-10-18","arxiv_id":"2110.09356","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":1,"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, 1 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-federated-bayesian-network-structure#ran","syntology_url":"https://syntology.ai/paper/2110.09356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09356"}},"official":null}},{"url":"/paper/adapt-to-adaptation-learning-personalization","slug":"adapt-to-adaptation-learning-personalization","title":"Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning","date":"2021-10-15","arxiv_id":"2110.08394","repositories_listed":2,"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/adapt-to-adaptation-learning-personalization#ran","syntology_url":"https://syntology.ai/paper/2110.08394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.08394"}},"official":{"repos":["ljaiverson/pfl-apple"],"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"]}}},{"url":"/paper/federated-learning-for-covid-19-detection","slug":"federated-learning-for-covid-19-detection","title":"Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing","date":"2021-10-14","arxiv_id":"2110.07136","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/federated-learning-for-covid-19-detection#ran","syntology_url":"https://syntology.ai/paper/2110.07136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07136"}},"official":{"repos":["dinhgit/FL-GAN_COVID"],"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/deep-federated-learning-for-autonomous","slug":"deep-federated-learning-for-autonomous","title":"Deep Federated Learning for Autonomous Driving","date":"2021-10-12","arxiv_id":"2110.05754","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/deep-federated-learning-for-autonomous#ran","syntology_url":"https://syntology.ai/paper/2110.05754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.05754"}},"official":{"repos":["aioz-ai/fadnet"],"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/progfed-effective-communication-and-1","slug":"progfed-effective-communication-and-1","title":"ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training","date":"2021-10-11","arxiv_id":"2110.05323","repositories_listed":2,"syntology":{"n":17,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 10 unverified","sample_list":"/paper/progfed-effective-communication-and-1#ran","syntology_url":"https://syntology.ai/paper/2110.05323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.05323"}},"official":{"repos":["a514514772/progfed","hui-po-wang/progfed"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/openfed-an-open-source-security-and-privacy","slug":"openfed-an-open-source-security-and-privacy","title":"OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework","date":"2021-09-16","arxiv_id":"2109.07852","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/openfed-an-open-source-security-and-privacy#ran","syntology_url":"https://syntology.ai/paper/2109.07852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07852"}},"official":{"repos":["federallab/openfed"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-fedrec-efficient-federated-learning","slug":"efficient-fedrec-efficient-federated-learning","title":"Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation","date":"2021-09-12","arxiv_id":"2109.05446","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":2,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"10 ran (of which 2 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/efficient-fedrec-efficient-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2109.05446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05446"}},"official":{"repos":["yjw1029/efficient-fedrec"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":2,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/flashe-additively-symmetric-homomorphic","slug":"flashe-additively-symmetric-homomorphic","title":"FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning","date":"2021-09-02","arxiv_id":"2109.00675","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/flashe-additively-symmetric-homomorphic#ran","syntology_url":"https://syntology.ai/paper/2109.00675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.00675"}},"official":null}},{"url":"/paper/federated-multi-task-learning-under-a-mixture","slug":"federated-multi-task-learning-under-a-mixture","title":"Federated Multi-Task Learning under a Mixture of Distributions","date":"2021-08-23","arxiv_id":"2108.10252","repositories_listed":5,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/federated-multi-task-learning-under-a-mixture#ran","syntology_url":"https://syntology.ai/paper/2108.10252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.10252"}},"official":{"repos":["omarfoq/fedem"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/fair-and-consistent-federated-learning","slug":"fair-and-consistent-federated-learning","title":"Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning","date":"2021-08-19","arxiv_id":"2108.08435","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":6,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fair-and-consistent-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2108.08435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08435"}},"official":{"repos":["cuis15/FCFL"],"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/multi-center-federated-learning-1","slug":"multi-center-federated-learning-1","title":"Multi-Center Federated Learning: Clients Clustering for Better Personalization","date":"2021-08-19","arxiv_id":"2108.08647","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-center-federated-learning-1#ran","syntology_url":"https://syntology.ai/paper/2108.08647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08647"}},"official":{"repos":["mingxuts/multi-center-fed-learning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/communication-efficient-federated-learning-8","slug":"communication-efficient-federated-learning-8","title":"EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning","date":"2021-08-19","arxiv_id":"2108.08842","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/communication-efficient-federated-learning-8#ran","syntology_url":"https://syntology.ai/paper/2108.08842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08842"}},"official":{"repos":["amitport/eden-distributed-mean-estimation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fed-tgan-federated-learning-framework-for","slug":"fed-tgan-federated-learning-framework-for","title":"Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data","date":"2021-08-18","arxiv_id":"2108.07927","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fed-tgan-federated-learning-framework-for#ran","syntology_url":"https://syntology.ai/paper/2108.07927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07927"}},"official":null}},{"url":"/paper/collaborative-unsupervised-visual","slug":"collaborative-unsupervised-visual","title":"Collaborative Unsupervised Visual Representation Learning from Decentralized Data","date":"2021-08-14","arxiv_id":"2108.06492","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/collaborative-unsupervised-visual#ran","syntology_url":"https://syntology.ai/paper/2108.06492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06492"}},"official":{"repos":["EasyFL-AI/EasyFL"],"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/fedpara-low-rank-hadamard-product","slug":"fedpara-low-rank-hadamard-product","title":"FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning","date":"2021-08-13","arxiv_id":"2108.06098","repositories_listed":2,"syntology":{"n":26,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":16,"n_honours":1,"n_violates":3,"n_no_contract":1,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 3 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/fedpara-low-rank-hadamard-product#ran","syntology_url":"https://syntology.ai/paper/2108.06098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06098"}},"official":{"repos":["south-hw/fedpara_iclr22"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/a-field-guide-to-federated-optimization","slug":"a-field-guide-to-federated-optimization","title":"A Field Guide to Federated Optimization","date":"2021-07-14","arxiv_id":"2107.06917","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/a-field-guide-to-federated-optimization#ran","syntology_url":"https://syntology.ai/paper/2107.06917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.06917"}},"official":{"repos":["google-research/federated"],"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":["listed","official"]}}},{"url":"/paper/personalized-federated-learning-via","slug":"personalized-federated-learning-via","title":"Sparse Personalized Federated Learning","date":"2021-07-12","arxiv_id":"2107.05330","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":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) · 1 unverified","sample_list":"/paper/personalized-federated-learning-via#ran","syntology_url":"https://syntology.ai/paper/2107.05330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05330"}},"official":null}},{"url":"/paper/rofl-attestable-robustness-for-secure","slug":"rofl-attestable-robustness-for-secure","title":"RoFL: Robustness of Secure Federated Learning","date":"2021-07-07","arxiv_id":"2107.03311","repositories_listed":2,"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":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) · 0 unverified","sample_list":"/paper/rofl-attestable-robustness-for-secure#ran","syntology_url":"https://syntology.ai/paper/2107.03311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.03311"}},"official":{"repos":["pps-lab/fl-analysis","pps-lab/rofl-project-code"],"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/on-bridging-generic-and-personalized","slug":"on-bridging-generic-and-personalized","title":"On Bridging Generic and Personalized Federated Learning for Image Classification","date":"2021-07-02","arxiv_id":"2107.00778","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/on-bridging-generic-and-personalized#ran","syntology_url":"https://syntology.ai/paper/2107.00778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00778"}},"official":{"repos":["hongyouc/fed-rod"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/gradient-leakage-resilient-federated-learning","slug":"gradient-leakage-resilient-federated-learning","title":"Gradient-Leakage Resilient Federated Learning","date":"2021-07-02","arxiv_id":"2107.01154","repositories_listed":2,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gradient-leakage-resilient-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2107.01154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01154"}},"official":{"repos":["git-disl/ESORICS20-CPL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/accumulative-poisoning-attacks-on-real-time","slug":"accumulative-poisoning-attacks-on-real-time","title":"Accumulative Poisoning Attacks on Real-time Data","date":"2021-06-18","arxiv_id":"2106.09993","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/accumulative-poisoning-attacks-on-real-time#ran","syntology_url":"https://syntology.ai/paper/2106.09993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09993"}},"official":{"repos":["ShawnXYang/AccumulativeAttack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-robustness-propagation-sharing","slug":"federated-robustness-propagation-sharing","title":"Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning","date":"2021-06-18","arxiv_id":"2106.10196","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/federated-robustness-propagation-sharing#ran","syntology_url":"https://syntology.ai/paper/2106.10196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10196"}},"official":{"repos":["illidanlab/FedRBN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/locally-differentially-private-federated","slug":"locally-differentially-private-federated","title":"Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses","date":"2021-06-17","arxiv_id":"2106.09779","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/locally-differentially-private-federated#ran","syntology_url":"https://syntology.ai/paper/2106.09779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09779"}},"official":{"repos":["lowya/Locally-Differentially-Private-Federated-Learning","lowya/private-federated-learning-without-a-trusted-server"],"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/optimal-accounting-of-differential-privacy","slug":"optimal-accounting-of-differential-privacy","title":"Optimal Accounting of Differential Privacy via Characteristic Function","date":"2021-06-16","arxiv_id":"2106.08567","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/optimal-accounting-of-differential-privacy#ran","syntology_url":"https://syntology.ai/paper/2106.08567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08567"}},"official":{"repos":["yuxiangw/autodp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/crfl-certifiably-robust-federated-learning","slug":"crfl-certifiably-robust-federated-learning","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","date":"2021-06-15","arxiv_id":"2106.08283","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/crfl-certifiably-robust-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2106.08283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08283"}},"official":{"repos":["AI-secure/CRFL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-architecture-design-for-tackling","slug":"rethinking-architecture-design-for-tackling","title":"Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning","date":"2021-06-10","arxiv_id":"2106.06047","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rethinking-architecture-design-for-tackling#ran","syntology_url":"https://syntology.ai/paper/2106.06047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06047"}},"official":{"repos":["Liangqiong/ViT-FL-main"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-disaggregation-breaking-privacy-in","slug":"gradient-disaggregation-breaking-privacy-in","title":"Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix","date":"2021-06-10","arxiv_id":"2106.06089","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":4,"n_violates":1,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 4 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gradient-disaggregation-breaking-privacy-in#ran","syntology_url":"https://syntology.ai/paper/2106.06089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06089"}},"official":{"repos":["gdisag/gradient_disaggregation"],"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/no-fear-of-heterogeneity-classifier","slug":"no-fear-of-heterogeneity-classifier","title":"No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data","date":"2021-06-09","arxiv_id":"2106.05001","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":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/no-fear-of-heterogeneity-classifier#ran","syntology_url":"https://syntology.ai/paper/2106.05001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05001"}},"official":null}},{"url":"/paper/preservation-of-the-global-knowledge-by-not","slug":"preservation-of-the-global-knowledge-by-not","title":"Preservation of the Global Knowledge by Not-True Distillation in Federated Learning","date":"2021-06-06","arxiv_id":"2106.03097","repositories_listed":3,"syntology":{"n":9,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 2 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) · 5 unverified","sample_list":"/paper/preservation-of-the-global-knowledge-by-not#ran","syntology_url":"https://syntology.ai/paper/2106.03097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03097"}},"official":{"repos":["Lee-Gihun/FedNTD"],"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":["listed","official"]}}},{"url":"/paper/fedbabu-towards-enhanced-representation-for","slug":"fedbabu-towards-enhanced-representation-for","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","date":"2021-06-04","arxiv_id":"2106.06042","repositories_listed":3,"syntology":{"n":9,"n_ran":5,"n_constructed":1,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 1 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) · 4 unverified","sample_list":"/paper/fedbabu-towards-enhanced-representation-for#ran","syntology_url":"https://syntology.ai/paper/2106.06042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06042"}},"official":{"repos":["jhoon-oh/fedbabu"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/fedscale-benchmarking-model-and-system","slug":"fedscale-benchmarking-model-and-system","title":"FedScale: Benchmarking Model and System Performance of Federated Learning at Scale","date":"2021-05-24","arxiv_id":"2105.11367","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fedscale-benchmarking-model-and-system#ran","syntology_url":"https://syntology.ai/paper/2105.11367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11367"}},"official":{"repos":["SymbioticLab/FedScale"],"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":["listed","official"]}}},{"url":"/paper/user-label-leakage-from-gradients-in","slug":"user-label-leakage-from-gradients-in","title":"User-Level Label Leakage from Gradients in Federated Learning","date":"2021-05-19","arxiv_id":"2105.09369","repositories_listed":2,"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/user-label-leakage-from-gradients-in#ran","syntology_url":"https://syntology.ai/paper/2105.09369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09369"}},"official":{"repos":["tklab-tud/llg"],"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/drive-one-bit-distributed-mean-estimation","slug":"drive-one-bit-distributed-mean-estimation","title":"DRIVE: One-bit Distributed Mean Estimation","date":"2021-05-18","arxiv_id":"2105.08339","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/drive-one-bit-distributed-mean-estimation#ran","syntology_url":"https://syntology.ai/paper/2105.08339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.08339"}},"official":{"repos":["amitport/drive-one-bit-distributed-mean-estimation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/the-federated-tumor-segmentation-fets","slug":"the-federated-tumor-segmentation-fets","title":"The Federated Tumor Segmentation (FeTS) Challenge","date":"2021-05-12","arxiv_id":"2105.05874","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-federated-tumor-segmentation-fets#ran","syntology_url":"https://syntology.ai/paper/2105.05874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05874"}},"official":{"repos":["FETS-AI/Challenge","FETS-AI/Front-End"],"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/see-through-gradients-image-batch-recovery","slug":"see-through-gradients-image-batch-recovery","title":"See through Gradients: Image Batch Recovery via GradInversion","date":"2021-04-15","arxiv_id":"2104.07586","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":1,"n_ran_checked":5,"n_instrument":7,"n_unverified":1,"n_honours":4,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"12 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 4 honoured, 0 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/see-through-gradients-image-batch-recovery#ran","syntology_url":"https://syntology.ai/paper/2104.07586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07586"}},"official":null}},{"url":"/paper/practical-defences-against-model-inversion","slug":"practical-defences-against-model-inversion","title":"Practical Defences Against Model Inversion Attacks for Split Neural Networks","date":"2021-04-12","arxiv_id":"2104.05743","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/practical-defences-against-model-inversion#ran","syntology_url":"https://syntology.ai/paper/2104.05743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.05743"}},"official":{"repos":["TTitcombe/Model-Inversion-SplitNN"],"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/pyvertical-a-vertical-federated-learning","slug":"pyvertical-a-vertical-federated-learning","title":"PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN","date":"2021-04-01","arxiv_id":"2104.00489","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":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/pyvertical-a-vertical-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2104.00489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00489"}},"official":{"repos":["OpenMined/PyVertical"],"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/model-contrastive-federated-learning","slug":"model-contrastive-federated-learning","title":"Model-Contrastive Federated Learning","date":"2021-03-30","arxiv_id":"2103.16257","repositories_listed":6,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/model-contrastive-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2103.16257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16257"}},"official":{"repos":["adap/flower"],"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":["listed","named_in_paper"]}}},{"url":"/paper/feddg-federated-domain-generalization-on","slug":"feddg-federated-domain-generalization-on","title":"FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space","date":"2021-03-10","arxiv_id":"2103.06030","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":2,"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/feddg-federated-domain-generalization-on#ran","syntology_url":"https://syntology.ai/paper/2103.06030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06030"}},"official":{"repos":["liuquande/FedDG-ELCFS"],"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"]}}},{"url":"/paper/moshpit-sgd-communication-efficient","slug":"moshpit-sgd-communication-efficient","title":"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices","date":"2021-03-04","arxiv_id":"2103.03239","repositories_listed":2,"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/moshpit-sgd-communication-efficient#ran","syntology_url":"https://syntology.ai/paper/2103.03239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03239"}},"official":{"repos":["yandex-research/moshpit-sgd"],"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/multi-institutional-collaborations-for","slug":"multi-institutional-collaborations-for","title":"Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning","date":"2021-03-03","arxiv_id":"2103.02148","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":0,"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/multi-institutional-collaborations-for#ran","syntology_url":"https://syntology.ai/paper/2103.02148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.02148"}},"official":{"repos":["guopengf/FL-MRCM"],"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/fjord-fair-and-accurate-federated-learning","slug":"fjord-fair-and-accurate-federated-learning","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","date":"2021-02-26","arxiv_id":"2102.13451","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fjord-fair-and-accurate-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2102.13451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.13451"}},"official":{"repos":["adap/flower"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/practical-and-private-deep-learning-without","slug":"practical-and-private-deep-learning-without","title":"Practical and Private (Deep) Learning without Sampling or Shuffling","date":"2021-02-26","arxiv_id":"2103.00039","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/practical-and-private-deep-learning-without#ran","syntology_url":"https://syntology.ai/paper/2103.00039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00039"}},"official":{"repos":["google-research/DP-FTRL","google-research/federated"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/label-leakage-and-protection-in-two-party","slug":"label-leakage-and-protection-in-two-party","title":"Label Leakage and Protection in Two-party Split Learning","date":"2021-02-17","arxiv_id":"2102.08504","repositories_listed":2,"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/label-leakage-and-protection-in-two-party#ran","syntology_url":"https://syntology.ai/paper/2102.08504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08504"}},"official":{"repos":["bytedance/fedlearner"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/fedbn-federated-learning-on-non-iid-features-1","slug":"fedbn-federated-learning-on-non-iid-features-1","title":"FedBN: Federated Learning on Non-IID Features via Local Batch Normalization","date":"2021-02-15","arxiv_id":"2102.07623","repositories_listed":4,"syntology":{"n":13,"n_ran":7,"n_constructed":3,"n_ran_checked":5,"n_instrument":2,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/fedbn-federated-learning-on-non-iid-features-1#ran","syntology_url":"https://syntology.ai/paper/2102.07623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.07623"}},"official":{"repos":["adap/flower","med-air/FedBN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/marina-faster-non-convex-distributed-learning","slug":"marina-faster-non-convex-distributed-learning","title":"MARINA: Faster Non-Convex Distributed Learning with Compression","date":"2021-02-15","arxiv_id":"2102.07845","repositories_listed":1,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":6,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":11,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/marina-faster-non-convex-distributed-learning#ran","syntology_url":"https://syntology.ai/paper/2102.07845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.07845"}},"official":{"repos":["burlachenkok/marina"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-deep-auc-maximization-for","slug":"federated-deep-auc-maximization-for","title":"Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity","date":"2021-02-09","arxiv_id":"2102.04635","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":2,"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/federated-deep-auc-maximization-for#ran","syntology_url":"https://syntology.ai/paper/2102.04635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04635"}},"official":{"repos":["optimization-ai/icml2021_feddeepauc_codasca"],"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"]}}},{"url":"/paper/capc-learning-confidential-and-private-1","slug":"capc-learning-confidential-and-private-1","title":"CaPC Learning: Confidential and Private Collaborative Learning","date":"2021-02-09","arxiv_id":"2102.05188","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/capc-learning-confidential-and-private-1#ran","syntology_url":"https://syntology.ai/paper/2102.05188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.05188"}},"official":{"repos":["cleverhans-lab/capc-iclr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-depression-detection-from-multi","slug":"federated-depression-detection-from-multi","title":"FedMood: Federated Learning on Mobile Health Data for Mood Detection","date":"2021-02-06","arxiv_id":"2102.09342","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/federated-depression-detection-from-multi#ran","syntology_url":"https://syntology.ai/paper/2102.09342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.09342"}},"official":{"repos":["RingBDStack/Fed_mood"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-on-non-iid-data-silos-an","slug":"federated-learning-on-non-iid-data-silos-an","title":"Federated Learning on Non-IID Data Silos: An Experimental Study","date":"2021-02-03","arxiv_id":"2102.02079","repositories_listed":4,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/federated-learning-on-non-iid-data-silos-an#ran","syntology_url":"https://syntology.ai/paper/2102.02079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.02079"}},"official":{"repos":["Xtra-Computing/NIID-Bench"],"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/hybrid-fl-algorithms-and-implementation","slug":"hybrid-fl-algorithms-and-implementation","title":"Hybrid Federated Learning: Algorithms and Implementation","date":"2020-12-22","arxiv_id":"2012.12420","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/hybrid-fl-algorithms-and-implementation#ran","syntology_url":"https://syntology.ai/paper/2012.12420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.12420"}},"official":null}},{"url":"/paper/personalized-federated-learning-with-first-1","slug":"personalized-federated-learning-with-first-1","title":"Personalized Federated Learning with First Order Model Optimization","date":"2020-12-15","arxiv_id":"2012.08565","repositories_listed":3,"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/personalized-federated-learning-with-first-1#ran","syntology_url":"https://syntology.ai/paper/2012.08565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.08565"}},"official":{"repos":["NVlabs/FedFomo"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/federated-multi-task-learning-for-competing","slug":"federated-multi-task-learning-for-competing","title":"Ditto: Fair and Robust Federated Learning Through Personalization","date":"2020-12-08","arxiv_id":"2012.04221","repositories_listed":4,"syntology":{"n":18,"n_ran":15,"n_constructed":5,"n_ran_checked":12,"n_instrument":3,"n_unverified":3,"n_honours":7,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"15 ran (of which 5 constructed an object rather than computing a result; 12 with no instrument failure: 7 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/federated-multi-task-learning-for-competing#ran","syntology_url":"https://syntology.ai/paper/2012.04221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.04221"}},"official":{"repos":["litian96/ditto"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":2,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/decentralizing-feature-extraction-with","slug":"decentralizing-feature-extraction-with","title":"Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition","date":"2020-10-26","arxiv_id":"2010.13309","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/decentralizing-feature-extraction-with#ran","syntology_url":"https://syntology.ai/paper/2010.13309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13309"}},"official":{"repos":["huckiyang/speech_quantum_dl"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/throughput-optimal-topology-design-for-cross","slug":"throughput-optimal-topology-design-for-cross","title":"Throughput-Optimal Topology Design for Cross-Silo Federated Learning","date":"2020-10-23","arxiv_id":"2010.12229","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/throughput-optimal-topology-design-for-cross#ran","syntology_url":"https://syntology.ai/paper/2010.12229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12229"}},"official":{"repos":["omarfoq/communication-in-cross-silo-fl"],"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/differentially-private-federated-linear","slug":"differentially-private-federated-linear","title":"Differentially-Private Federated Linear Bandits","date":"2020-10-22","arxiv_id":"2010.11425","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/differentially-private-federated-linear#ran","syntology_url":"https://syntology.ai/paper/2010.11425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11425"}},"official":{"repos":["abhimanyudubey/private_federated_linear_bandits"],"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/federated-learning-via-posterior-averaging-a-1","slug":"federated-learning-via-posterior-averaging-a-1","title":"Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms","date":"2020-10-11","arxiv_id":"2010.05273","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":5,"n_ran_checked":6,"n_instrument":4,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"10 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/federated-learning-via-posterior-averaging-a-1#ran","syntology_url":"https://syntology.ai/paper/2010.05273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05273"}},"official":{"repos":["alshedivat/fedpa"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":10,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/federated-learning-using-a-mixture-of-experts","slug":"federated-learning-using-a-mixture-of-experts","title":"Specialized federated learning using a mixture of experts","date":"2020-10-05","arxiv_id":"2010.02056","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/federated-learning-using-a-mixture-of-experts#ran","syntology_url":"https://syntology.ai/paper/2010.02056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02056"}},"official":{"repos":["edvinli/federated-learning-mixture"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/heterofl-computation-and-communication-1","slug":"heterofl-computation-and-communication-1","title":"HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients","date":"2020-10-03","arxiv_id":"2010.01264","repositories_listed":3,"syntology":{"n":13,"n_ran":5,"n_constructed":1,"n_ran_checked":4,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 1 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) · 8 unverified","sample_list":"/paper/heterofl-computation-and-communication-1#ran","syntology_url":"https://syntology.ai/paper/2010.01264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01264"}},"official":{"repos":["diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/flame-differentially-private-federated","slug":"flame-differentially-private-federated","title":"FLAME: Differentially Private Federated Learning in the Shuffle Model","date":"2020-09-17","arxiv_id":"2009.08063","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/flame-differentially-private-federated#ran","syntology_url":"https://syntology.ai/paper/2009.08063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08063"}},"official":null}},{"url":"/paper/feddistill-making-bayesian-model-ensemble","slug":"feddistill-making-bayesian-model-ensemble","title":"FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning","date":"2020-09-04","arxiv_id":"2009.01974","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/feddistill-making-bayesian-model-ensemble#ran","syntology_url":"https://syntology.ai/paper/2009.01974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.01974"}},"official":{"repos":["hongyouc/fedbe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-semi-supervised-federated","slug":"benchmarking-semi-supervised-federated","title":"Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models","date":"2020-08-26","arxiv_id":"2008.11364","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/benchmarking-semi-supervised-federated#ran","syntology_url":"https://syntology.ai/paper/2008.11364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11364"}},"official":{"repos":["jhcknzzm/SSFL-Benchmarking-Semi-supervised-Federated-Learning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/waffle-watermarking-in-federated-learning","slug":"waffle-watermarking-in-federated-learning","title":"WAFFLE: Watermarking in Federated Learning","date":"2020-08-17","arxiv_id":"2008.07298","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"10 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/waffle-watermarking-in-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2008.07298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07298"}},"official":{"repos":["ssg-research/WAFFLE"],"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/distantly-supervised-relation-extraction-in","slug":"distantly-supervised-relation-extraction-in","title":"Distantly Supervised Relation Extraction in Federated Settings","date":"2020-08-12","arxiv_id":"2008.05049","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/distantly-supervised-relation-extraction-in#ran","syntology_url":"https://syntology.ai/paper/2008.05049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05049"}},"official":{"repos":["DianboWork/FedDS"],"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","unlocated"]}}},{"url":"/paper/mime-mimicking-centralized-stochastic","slug":"mime-mimicking-centralized-stochastic","title":"Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning","date":"2020-08-08","arxiv_id":"2008.03606","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":0,"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/mime-mimicking-centralized-stochastic#ran","syntology_url":"https://syntology.ai/paper/2008.03606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.03606"}},"official":null}},{"url":"/paper/dynamic-federated-learning-model-for","slug":"dynamic-federated-learning-model-for","title":"Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning","date":"2020-07-29","arxiv_id":"2007.15030","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/dynamic-federated-learning-model-for#ran","syntology_url":"https://syntology.ai/paper/2007.15030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15030"}},"official":{"repos":["ari-dasci/s-ddaba"],"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/flower-a-friendly-federated-learning-research","slug":"flower-a-friendly-federated-learning-research","title":"Flower: A Friendly Federated Learning Research Framework","date":"2020-07-28","arxiv_id":"2007.14390","repositories_listed":3,"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/flower-a-friendly-federated-learning-research#ran","syntology_url":"https://syntology.ai/paper/2007.14390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.14390"}},"official":{"repos":["adap/flower"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/group-knowledge-transfer-collaborative","slug":"group-knowledge-transfer-collaborative","title":"Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge","date":"2020-07-28","arxiv_id":"2007.14513","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/group-knowledge-transfer-collaborative#ran","syntology_url":"https://syntology.ai/paper/2007.14513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.14513"}},"official":{"repos":["FedML-AI/FedML"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/fedml-a-research-library-and-benchmark-for","slug":"fedml-a-research-library-and-benchmark-for","title":"FedML: A Research Library and Benchmark for Federated Machine Learning","date":"2020-07-27","arxiv_id":"2007.13518","repositories_listed":5,"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":1,"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/fedml-a-research-library-and-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2007.13518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13518"}},"official":{"repos":["chaoyanghe/Awesome-Federated-Learning"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/data-poisoning-attacks-against-federated","slug":"data-poisoning-attacks-against-federated","title":"Data Poisoning Attacks Against Federated Learning Systems","date":"2020-07-16","arxiv_id":"2007.08432","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/data-poisoning-attacks-against-federated#ran","syntology_url":"https://syntology.ai/paper/2007.08432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08432"}},"official":{"repos":["git-disl/DataPoisoning_FL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"729fbd118134fff9803d744ae27847b668f6873b1cfc34849fa6f11ca3f80525","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}