{"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/5","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":68,"rows_per_page":100,"rows":[401,500],"of":6771,"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","prev":"/task/federated-learning/papers/4","next":"/task/federated-learning/papers/6","papers":[{"url":"/paper/fedkd-hybrid-federated-hybrid-knowledge","slug":"fedkd-hybrid-federated-hybrid-knowledge","title":"FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection","date":"2025-01-07","arxiv_id":"2501.04066","repositories_listed":1,"syntology":null},{"url":"/paper/over-the-air-fair-federated-learning-via","slug":"over-the-air-fair-federated-learning-via","title":"Over-the-Air Fair Federated Learning via Multi-Objective Optimization","date":"2025-01-06","arxiv_id":"2501.03392","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-byzantine-robustness-in-federated","slug":"rethinking-byzantine-robustness-in-federated","title":"Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective","date":"2025-01-06","arxiv_id":"2501.03301","repositories_listed":1,"syntology":null},{"url":"/paper/subspace-constraint-and-contribution","slug":"subspace-constraint-and-contribution","title":"Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-in-net-zero-microgrids-a-study","slug":"generalizing-in-net-zero-microgrids-a-study","title":"Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO","date":"2024-12-30","arxiv_id":"2412.20946","repositories_listed":1,"syntology":null},{"url":"/paper/calibre-towards-fair-and-accurate","slug":"calibre-towards-fair-and-accurate","title":"Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning","date":"2024-12-28","arxiv_id":"2412.20020","repositories_listed":1,"syntology":null},{"url":"/paper/federated-unlearning-with-gradient-descent","slug":"federated-unlearning-with-gradient-descent","title":"Federated Unlearning with Gradient Descent and Conflict Mitigation","date":"2024-12-28","arxiv_id":"2412.20200","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetrical-reciprocity-based-federated","slug":"asymmetrical-reciprocity-based-federated","title":"Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis","date":"2024-12-27","arxiv_id":"2412.19654","repositories_listed":1,"syntology":null},{"url":"/paper/fedcfa-alleviating-simpson-s-paradox-in-model","slug":"fedcfa-alleviating-simpson-s-paradox-in-model","title":"FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning","date":"2024-12-25","arxiv_id":"2412.18904","repositories_listed":1,"syntology":null},{"url":"/paper/asynchronous-federated-learning-a-scalable","slug":"asynchronous-federated-learning-a-scalable","title":"Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning","date":"2024-12-23","arxiv_id":"2412.17723","repositories_listed":1,"syntology":null},{"url":"/paper/label-privacy-in-split-learning-for-large","slug":"label-privacy-in-split-learning-for-large","title":"Label Privacy in Split Learning for Large Models with Parameter-Efficient Training","date":"2024-12-21","arxiv_id":"2412.16669","repositories_listed":1,"syntology":null},{"url":"/paper/fluke-federated-learning-utility-framework","slug":"fluke-federated-learning-utility-framework","title":"fluke: Federated Learning Utility frameworK for Experimentation and research","date":"2024-12-20","arxiv_id":"2412.15728","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-robustness-of-distributed-machine","slug":"on-the-robustness-of-distributed-machine","title":"On the Robustness of Distributed Machine Learning against Transfer Attacks","date":"2024-12-18","arxiv_id":"2412.14080","repositories_listed":1,"syntology":null},{"url":"/paper/semidfl-a-semi-supervised-paradigm-for","slug":"semidfl-a-semi-supervised-paradigm-for","title":"SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning","date":"2024-12-18","arxiv_id":"2412.13589","repositories_listed":1,"syntology":null},{"url":"/paper/splitfedzip-learned-compression-for-data","slug":"splitfedzip-learned-compression-for-data","title":"SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning","date":"2024-12-18","arxiv_id":"2412.17150","repositories_listed":1,"syntology":null},{"url":"/paper/fedcar-cross-client-adaptive-re-weighting-for","slug":"fedcar-cross-client-adaptive-re-weighting-for","title":"FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning","date":"2024-12-16","arxiv_id":"2412.11463","repositories_listed":1,"syntology":null},{"url":"/paper/just-a-simple-transformation-is-enough-for","slug":"just-a-simple-transformation-is-enough-for","title":"Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning","date":"2024-12-16","arxiv_id":"2412.11689","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-inter-intra-heterogeneity-for-graph","slug":"modeling-inter-intra-heterogeneity-for-graph","title":"Modeling Inter-Intra Heterogeneity for Graph Federated Learning","date":"2024-12-16","arxiv_id":"2412.11402","repositories_listed":1,"syntology":null},{"url":"/paper/vertical-federated-unlearning-via-backdoor","slug":"vertical-federated-unlearning-via-backdoor","title":"Vertical Federated Unlearning via Backdoor Certification","date":"2024-12-16","arxiv_id":"2412.11476","repositories_listed":1,"syntology":null},{"url":"/paper/task-diversity-in-bayesian-federated-learning","slug":"task-diversity-in-bayesian-federated-learning","title":"Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression","date":"2024-12-14","arxiv_id":"2412.10897","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-causal-discovery-in-dynamic-bayesian","slug":"temporal-causal-discovery-in-dynamic-bayesian","title":"Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data","date":"2024-12-13","arxiv_id":"2412.09814","repositories_listed":1,"syntology":null},{"url":"/paper/federated-foundation-models-on-heterogeneous","slug":"federated-foundation-models-on-heterogeneous","title":"Federated Foundation Models on Heterogeneous Time Series","date":"2024-12-12","arxiv_id":"2412.08906","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/federated-foundation-models-on-heterogeneous#ran","syntology_url":"https://syntology.ai/paper/2412.08906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08906"}},"official":{"repos":["shengchaochen82/FFTS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-federated-learning-for-semantic","slug":"benchmarking-federated-learning-for-semantic","title":"Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation","date":"2024-12-11","arxiv_id":"2412.10436","repositories_listed":1,"syntology":null},{"url":"/paper/learn-how-to-query-from-unlabeled-data","slug":"learn-how-to-query-from-unlabeled-data","title":"Learn How to Query from Unlabeled Data Streams in Federated Learning","date":"2024-12-11","arxiv_id":"2412.08138","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-federated-learning-framework-against","slug":"a-new-federated-learning-framework-against","title":"A New Federated Learning Framework Against Gradient Inversion Attacks","date":"2024-12-10","arxiv_id":"2412.07187","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-sparse-customization-in","slug":"learnable-sparse-customization-in","title":"Learnable Sparse Customization in Heterogeneous Edge Computing","date":"2024-12-10","arxiv_id":"2412.07216","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-personalized-federated-learning","slug":"optimizing-personalized-federated-learning","title":"Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning","date":"2024-12-10","arxiv_id":"2412.07062","repositories_listed":1,"syntology":null},{"url":"/paper/dapperfl-domain-adaptive-federated-learning","slug":"dapperfl-domain-adaptive-federated-learning","title":"DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices","date":"2024-12-08","arxiv_id":"2412.05823","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/dapperfl-domain-adaptive-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2412.05823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05823"}},"official":{"repos":["jyzgh/dapperfl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/one-shot-federated-learning-via-synthetic","slug":"one-shot-federated-learning-via-synthetic","title":"One-shot Federated Learning via Synthetic Distiller-Distillate Communication","date":"2024-12-06","arxiv_id":"2412.05186","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":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/one-shot-federated-learning-via-synthetic#ran","syntology_url":"https://syntology.ai/paper/2412.05186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05186"}},"official":{"repos":["carkham/fedsd2c"],"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/befl-balancing-energy-consumption-in","slug":"befl-balancing-energy-consumption-in","title":"BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT","date":"2024-12-05","arxiv_id":"2412.03950","repositories_listed":1,"syntology":null},{"url":"/paper/feddual-a-dual-strategy-with-adaptive-loss","slug":"feddual-a-dual-strategy-with-adaptive-loss","title":"FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning","date":"2024-12-05","arxiv_id":"2412.04416","repositories_listed":1,"syntology":null},{"url":"/paper/feddw-distilling-weights-through-consistency","slug":"feddw-distilling-weights-through-consistency","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","date":"2024-12-05","arxiv_id":"2412.04521","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-in-mobile-networks-a","slug":"federated-learning-in-mobile-networks-a","title":"Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting","date":"2024-12-05","arxiv_id":"2412.04081","repositories_listed":1,"syntology":null},{"url":"/paper/reactive-orchestration-for-hierarchical","slug":"reactive-orchestration-for-hierarchical","title":"Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budget","date":"2024-12-04","arxiv_id":"2412.03385","repositories_listed":1,"syntology":null},{"url":"/paper/defending-against-diverse-attacks-in","slug":"defending-against-diverse-attacks-in","title":"Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization","date":"2024-12-03","arxiv_id":"2412.02535","repositories_listed":1,"syntology":null},{"url":"/paper/towards-the-efficacy-of-federated-prediction","slug":"towards-the-efficacy-of-federated-prediction","title":"Towards the efficacy of federated prediction for epidemics on networks","date":"2024-12-03","arxiv_id":"2412.02161","repositories_listed":1,"syntology":null},{"url":"/paper/fedah-aggregated-head-for-personalized","slug":"fedah-aggregated-head-for-personalized","title":"FedAH: Aggregated Head for Personalized Federated Learning","date":"2024-12-02","arxiv_id":"2412.01295","repositories_listed":1,"syntology":null},{"url":"/paper/federated-motor-imagery-classification-for","slug":"federated-motor-imagery-classification-for","title":"Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces","date":"2024-12-02","arxiv_id":"2412.01079","repositories_listed":1,"syntology":null},{"url":"/paper/fedpaw-federated-learning-with-personalized","slug":"fedpaw-federated-learning-with-personalized","title":"FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction","date":"2024-12-02","arxiv_id":"2412.01281","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-guide-to-explainable-ai-from","slug":"a-comprehensive-guide-to-explainable-ai-from","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","date":"2024-12-01","arxiv_id":"2412.00800","repositories_listed":1,"syntology":null},{"url":"/paper/controlling-participation-in-federated","slug":"controlling-participation-in-federated","title":"Controlling Participation in Federated Learning with Feedback","date":"2024-11-28","arxiv_id":"2411.19242","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-federated-fine-tuning-for-llms","slug":"personalized-federated-fine-tuning-for-llms","title":"Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures","date":"2024-11-28","arxiv_id":"2411.19128","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-client-selection-with","slug":"adaptive-client-selection-with","title":"Adaptive Client Selection with Personalization for Communication Efficient Federated Learning","date":"2024-11-26","arxiv_id":"2411.17833","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-sign-momentum-with-local-steps","slug":"distributed-sign-momentum-with-local-steps","title":"Distributed Sign Momentum with Local Steps for Training Transformers","date":"2024-11-26","arxiv_id":"2411.17866","repositories_listed":1,"syntology":null},{"url":"/paper/tifed-a-tiny-integer-based-federated-learning","slug":"tifed-a-tiny-integer-based-federated-learning","title":"TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment","date":"2024-11-25","arxiv_id":"2411.16442","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-in-chemical-engineering-a","slug":"federated-learning-in-chemical-engineering-a","title":"Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources","date":"2024-11-23","arxiv_id":"2411.16737","repositories_listed":1,"syntology":null},{"url":"/paper/fedmllm-federated-fine-tuning-mllm-on","slug":"fedmllm-federated-fine-tuning-mllm-on","title":"FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data","date":"2024-11-22","arxiv_id":"2411.14717","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedmllm-federated-fine-tuning-mllm-on#ran","syntology_url":"https://syntology.ai/paper/2411.14717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.14717"}},"official":{"repos":["1xbq1/fedmllm"],"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/geminio-language-guided-gradient-inversion","slug":"geminio-language-guided-gradient-inversion","title":"Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning","date":"2024-11-22","arxiv_id":"2411.14937","repositories_listed":1,"syntology":null},{"url":"/paper/refol-resource-efficient-federated-online","slug":"refol-resource-efficient-federated-online","title":"REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting","date":"2024-11-21","arxiv_id":"2411.14046","repositories_listed":1,"syntology":null},{"url":"/paper/on-device-content-based-recommendation-with","slug":"on-device-content-based-recommendation-with","title":"On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective","date":"2024-11-20","arxiv_id":"2411.13052","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-inference-attacks-for-federated","slug":"attribute-inference-attacks-for-federated","title":"Attribute Inference Attacks for Federated Regression Tasks","date":"2024-11-19","arxiv_id":"2411.12697","repositories_listed":1,"syntology":null},{"url":"/paper/a-potential-game-perspective-in-federated","slug":"a-potential-game-perspective-in-federated","title":"A Potential Game Perspective in Federated Learning","date":"2024-11-18","arxiv_id":"2411.11793","repositories_listed":1,"syntology":null},{"url":"/paper/freezing-of-gait-detection-using-gramian","slug":"freezing-of-gait-detection-using-gramian","title":"Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors","date":"2024-11-18","arxiv_id":"2411.11764","repositories_listed":1,"syntology":null},{"url":"/paper/embedding-byzantine-fault-tolerance-into","slug":"embedding-byzantine-fault-tolerance-into","title":"Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring Plugin","date":"2024-11-15","arxiv_id":"2411.10212","repositories_listed":1,"syntology":null},{"url":"/paper/fedali-personalized-federated-learning-with","slug":"fedali-personalized-federated-learning-with","title":"FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport","date":"2024-11-15","arxiv_id":"2411.10595","repositories_listed":1,"syntology":null},{"url":"/paper/framework-for-co-distillation-driven","slug":"framework-for-co-distillation-driven","title":"Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare","date":"2024-11-15","arxiv_id":"2411.10383","repositories_listed":1,"syntology":null},{"url":"/paper/a-stochastic-optimization-framework-for","slug":"a-stochastic-optimization-framework-for","title":"A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data","date":"2024-11-12","arxiv_id":"2411.07889","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-client-pruning-for-noisy","slug":"federated-learning-client-pruning-for-noisy","title":"Federated Learning Client Pruning for Noisy Labels","date":"2024-11-11","arxiv_id":"2411.07391","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-ensembling-in-one-shot-federated","slug":"revisiting-ensembling-in-one-shot-federated","title":"Revisiting Ensembling in One-Shot Federated Learning","date":"2024-11-11","arxiv_id":"2411.07182","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":1,"n_ran_checked":4,"n_instrument":4,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":10,"phrase":"8 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revisiting-ensembling-in-one-shot-federated#ran","syntology_url":"https://syntology.ai/paper/2411.07182","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07182"}},"official":{"repos":["sacs-epfl/fens"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tinyml-nlp-approach-for-semantic-wireless","slug":"tinyml-nlp-approach-for-semantic-wireless","title":"TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation","date":"2024-11-09","arxiv_id":"2411.06291","repositories_listed":1,"syntology":null},{"url":"/paper/fedrise-rating-induced-sign-election-of","slug":"fedrise-rating-induced-sign-election-of","title":"FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning","date":"2024-11-06","arxiv_id":"2411.03861","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-defenses-against-gradient","slug":"optimal-defenses-against-gradient","title":"Optimal Defenses Against Gradient Reconstruction Attacks","date":"2024-11-06","arxiv_id":"2411.03746","repositories_listed":1,"syntology":null},{"url":"/paper/fedlad-federated-evaluation-of-deep-leakage","slug":"fedlad-federated-evaluation-of-deep-leakage","title":"FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses","date":"2024-11-05","arxiv_id":"2411.03019","repositories_listed":1,"syntology":null},{"url":"/paper/query-efficient-adversarial-attack-against","slug":"query-efficient-adversarial-attack-against","title":"Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning","date":"2024-11-05","arxiv_id":"2411.02809","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-structured-pruning-for-efficient","slug":"automatic-structured-pruning-for-efficient","title":"Automatic Structured Pruning for Efficient Architecture in Federated Learning","date":"2024-11-04","arxiv_id":"2411.01759","repositories_listed":1,"syntology":null},{"url":"/paper/fedrema-improving-personalized-federated","slug":"fedrema-improving-personalized-federated","title":"FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients","date":"2024-11-04","arxiv_id":"2411.01825","repositories_listed":1,"syntology":null},{"url":"/paper/fppl-an-efficient-and-non-iid-robust","slug":"fppl-an-efficient-and-non-iid-robust","title":"FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework","date":"2024-11-04","arxiv_id":"2411.01904","repositories_listed":1,"syntology":null},{"url":"/paper/masked-autoencoders-are-parameter-efficient","slug":"masked-autoencoders-are-parameter-efficient","title":"Masked Autoencoders are Parameter-Efficient Federated Continual Learners","date":"2024-11-04","arxiv_id":"2411.01916","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-robust-regularized-federated","slug":"efficient-and-robust-regularized-federated","title":"Efficient and Robust Regularized Federated Recommendation","date":"2024-11-03","arxiv_id":"2411.01540","repositories_listed":1,"syntology":null},{"url":"/paper/c2a-client-customized-adaptation-for","slug":"c2a-client-customized-adaptation-for","title":"C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning","date":"2024-11-01","arxiv_id":"2411.00311","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/c2a-client-customized-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2411.00311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.00311"}},"official":{"repos":["yeachan-kr/c2a"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/identify-backdoored-model-in-federated","slug":"identify-backdoored-model-in-federated","title":"Identify Backdoored Model in Federated Learning via Individual Unlearning","date":"2024-11-01","arxiv_id":"2411.01040","repositories_listed":1,"syntology":null},{"url":"/paper/federated-black-box-adaptation-for-semantic","slug":"federated-black-box-adaptation-for-semantic","title":"Federated Black-Box Adaptation for Semantic Segmentation","date":"2024-10-31","arxiv_id":"2410.24181","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 7 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; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/federated-black-box-adaptation-for-semantic#ran","syntology_url":"https://syntology.ai/paper/2410.24181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24181"}},"official":{"repos":["JayParanjape/blackfed"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-ai-powered-plugin-for-robust","slug":"generative-ai-powered-plugin-for-robust","title":"Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks","date":"2024-10-31","arxiv_id":"2410.23824","repositories_listed":1,"syntology":null},{"url":"/paper/local-superior-soups-a-catalyst-for-model","slug":"local-superior-soups-a-catalyst-for-model","title":"Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning","date":"2024-10-31","arxiv_id":"2410.23660","repositories_listed":1,"syntology":{"n":12,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":12,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/local-superior-soups-a-catalyst-for-model#ran","syntology_url":"https://syntology.ai/paper/2410.23660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23660"}},"official":{"repos":["ubc-tea/local-superior-soups"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-under-periodic-client","slug":"federated-learning-under-periodic-client","title":"Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis","date":"2024-10-30","arxiv_id":"2410.23131","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/federated-learning-under-periodic-client#ran","syntology_url":"https://syntology.ai/paper/2410.23131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23131"}},"official":{"repos":["MingruiLiu-ML-Lab/FL-under-Periodic-Participation"],"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/fisc-federated-domain-generalization-via","slug":"fisc-federated-domain-generalization-via","title":"PARDON: Privacy-Aware and Robust Federated Domain Generalization","date":"2024-10-30","arxiv_id":"2410.22622","repositories_listed":1,"syntology":null},{"url":"/paper/fl-2-overcoming-few-labels-in-federated-semi","slug":"fl-2-overcoming-few-labels-in-federated-semi","title":"(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning","date":"2024-10-30","arxiv_id":"2410.23227","repositories_listed":1,"syntology":{"n":18,"n_ran":9,"n_constructed":2,"n_ran_checked":3,"n_instrument":6,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/fl-2-overcoming-few-labels-in-federated-semi#ran","syntology_url":"https://syntology.ai/paper/2410.23227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23227"}},"official":{"repos":["seungjoo-ai/FLFL-NeurIPS24"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["community","official"]}}},{"url":"/paper/vertical-federated-learning-with-missing","slug":"vertical-federated-learning-with-missing","title":"Vertical Federated Learning with Missing Features During Training and Inference","date":"2024-10-29","arxiv_id":"2410.22564","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/vertical-federated-learning-with-missing#ran","syntology_url":"https://syntology.ai/paper/2410.22564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.22564"}},"official":{"repos":["valdeira/laser-vfl"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-statistical-analysis-of-deep-federated","slug":"a-statistical-analysis-of-deep-federated","title":"A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data","date":"2024-10-28","arxiv_id":"2410.20659","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-solution-to-diverse-heterogeneities","slug":"a-unified-solution-to-diverse-heterogeneities","title":"A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning","date":"2024-10-28","arxiv_id":"2410.21119","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-federated-learning-with-mixture","slug":"personalized-federated-learning-with-mixture","title":"Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning","date":"2024-10-28","arxiv_id":"2410.21547","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/personalized-federated-learning-with-mixture#ran","syntology_url":"https://syntology.ai/paper/2410.21547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21547"}},"official":{"repos":["pouyamghari/Fed-POE"],"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/fusefl-one-shot-federated-learning-through","slug":"fusefl-one-shot-federated-learning-through","title":"FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion","date":"2024-10-27","arxiv_id":"2410.20380","repositories_listed":1,"syntology":null},{"url":"/paper/classifier-clustering-and-feature-alignment","slug":"classifier-clustering-and-feature-alignment","title":"Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift","date":"2024-10-24","arxiv_id":"2410.18478","repositories_listed":1,"syntology":null},{"url":"/paper/federated-transformer-multi-party-vertical","slug":"federated-transformer-multi-party-vertical","title":"Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data","date":"2024-10-23","arxiv_id":"2410.17986","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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/federated-transformer-multi-party-vertical#ran","syntology_url":"https://syntology.ai/paper/2410.17986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.17986"}},"official":{"repos":["xtra-computing/fet"],"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/comparative-evaluation-of-clustered-federated","slug":"comparative-evaluation-of-clustered-federated","title":"Comparative Evaluation of Clustered Federated Learning Methods","date":"2024-10-18","arxiv_id":"2410.14212","repositories_listed":1,"syntology":null},{"url":"/paper/fedmse-federated-learning-for-iot-network","slug":"fedmse-federated-learning-for-iot-network","title":"FedMSE: Federated learning for IoT network intrusion detection","date":"2024-10-18","arxiv_id":"2410.14121","repositories_listed":1,"syntology":null},{"url":"/paper/deer-deviation-eliminating-and-noise","slug":"deer-deviation-eliminating-and-noise","title":"DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation","date":"2024-10-16","arxiv_id":"2410.12926","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"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) · 3 unverified","sample_list":"/paper/deer-deviation-eliminating-and-noise#ran","syntology_url":"https://syntology.ai/paper/2410.12926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12926"}},"official":{"repos":["cuhk-aim-group/deer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fedcap-robust-federated-learning-via","slug":"fedcap-robust-federated-learning-via","title":"FedCAP: Robust Federated Learning via Customized Aggregation and Personalization","date":"2024-10-16","arxiv_id":"2410.13083","repositories_listed":1,"syntology":null},{"url":"/paper/adversarially-guided-stateful-defense-against","slug":"adversarially-guided-stateful-defense-against","title":"Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning","date":"2024-10-15","arxiv_id":"2410.11205","repositories_listed":1,"syntology":null},{"url":"/paper/fedccrl-federated-domain-generalization-with","slug":"fedccrl-federated-domain-generalization-with","title":"FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning","date":"2024-10-15","arxiv_id":"2410.11267","repositories_listed":1,"syntology":null},{"url":"/paper/foogd-federated-collaboration-for-both-out-of","slug":"foogd-federated-collaboration-for-both-out-of","title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","date":"2024-10-15","arxiv_id":"2410.11397","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"10 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/foogd-federated-collaboration-for-both-out-of#ran","syntology_url":"https://syntology.ai/paper/2410.11397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11397"}},"official":{"repos":["xenialll/foogd-main"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/why-go-full-elevating-federated-learning","slug":"why-go-full-elevating-federated-learning","title":"Why Go Full? Elevating Federated Learning Through Partial Network Updates","date":"2024-10-15","arxiv_id":"2410.11559","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"7 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; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/why-go-full-elevating-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2410.11559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11559"}},"official":{"repos":["FLAIR-Community/Fling"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-experts-made-personalized","slug":"mixture-of-experts-made-personalized","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","date":"2024-10-14","arxiv_id":"2410.10114","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mixture-of-experts-made-personalized#ran","syntology_url":"https://syntology.ai/paper/2410.10114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10114"}},"official":{"repos":["ljaiverson/pfedmoap"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-new-perspective-to-boost-performance","slug":"a-new-perspective-to-boost-performance","title":"A New Perspective to Boost Performance Fairness for Medical Federated Learning","date":"2024-10-12","arxiv_id":"2410.19765","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-federated-kolmogorov-arnold","slug":"evaluating-federated-kolmogorov-arnold","title":"Evaluating Federated Kolmogorov-Arnold Networks on Non-IID Data","date":"2024-10-11","arxiv_id":"2410.08961","repositories_listed":1,"syntology":null},{"url":"/paper/gradients-stand-in-for-defending-deep-leakage","slug":"gradients-stand-in-for-defending-deep-leakage","title":"Gradients Stand-in for Defending Deep Leakage in Federated Learning","date":"2024-10-11","arxiv_id":"2410.08734","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-personalization-in-fedprox-a","slug":"the-effect-of-personalization-in-fedprox-a","title":"Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees","date":"2024-10-11","arxiv_id":"2410.08934","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":8,"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/the-effect-of-personalization-in-fedprox-a#ran","syntology_url":"https://syntology.ai/paper/2410.08934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08934"}},"official":null}},{"url":"/paper/adaptive-active-inference-agents-for","slug":"adaptive-active-inference-agents-for","title":"Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning","date":"2024-10-09","arxiv_id":"2410.09099","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-data-heterogeneity-evaluation","slug":"benchmarking-data-heterogeneity-evaluation","title":"Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning","date":"2024-10-09","arxiv_id":"2410.07286","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/benchmarking-data-heterogeneity-evaluation#ran","syntology_url":"https://syntology.ai/paper/2410.07286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07286"}},"official":{"repos":["xiaoni-61/dh-benchmark"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fedl2g-learning-to-guide-local-training-in","slug":"fedl2g-learning-to-guide-local-training-in","title":"Adaptive Guidance for Local Training in Heterogeneous Federated Learning","date":"2024-10-09","arxiv_id":"2410.06490","repositories_listed":1,"syntology":null}],"record_sha256":"8ada784fcfe526f10e7e388276afc68f98ed1d59b4d3b7fcd6d3484e9e49b27b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}