{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/fedavg","entry":"FedAvg","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":21,"n_papers_ran":15,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":17,"n_samples_ran":8,"n_samples_fingerprinted":0,"n_places":27,"n_places_pointer_only":11,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":5,"ran_fixture":0,"ran":3,"unverified":9},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2603.20283","paper":"/paper/arxiv-2603-20283","title":"FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing ⋆","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"yanzhenxing123/FastPFRec","path":"model/graph/PerFedRec.py","file_url":"https://github.com/yanzhenxing123/FastPFRec/blob/HEAD/model/graph/PerFedRec.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cddf6b6e878cc6ae","mcp_get_code":{"code_sha256":"cddf6b6e878cc6ae"}},{"arxiv_id":"2603.20283","paper":"/paper/arxiv-2603-20283","title":"FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing ⋆","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"yanzhenxing123/FastPFRec","path":"model/graph/FastPFRec.py","file_url":"https://github.com/yanzhenxing123/FastPFRec/blob/HEAD/model/graph/FastPFRec.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"2509.20383","paper":"/paper/arxiv-2509-20383","title":"MARS: A Malignity-Aware Backdoor Defense in Federated Learning","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"yunming181920/MARS","path":"defenses/mars.py","file_url":"https://github.com/yunming181920/MARS/blob/HEAD/defenses/mars.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"05179368187f7e1e","mcp_get_code":{"code_sha256":"05179368187f7e1e"}},{"arxiv_id":"2409.05976","paper":"/paper/flora-federated-fine-tuning-large-language","title":"FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations","date":"2024-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atp-1010/federatedllm","path":"fed_utils/model_aggregation.py","file_url":"https://github.com/atp-1010/federatedllm/blob/HEAD/fed_utils/model_aggregation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"516cac45490ea6a7","mcp_get_code":{"code_sha256":"516cac45490ea6a7"}},{"arxiv_id":"2408.04301","paper":"/paper/tackling-noisy-clients-in-federated-learning","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sprinter1999/fedelc","path":"fl_models/fed.py","file_url":"https://github.com/sprinter1999/fedelc/blob/HEAD/fl_models/fed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee28ff7729e298ee","mcp_get_code":{"code_sha256":"ee28ff7729e298ee"}},{"arxiv_id":"2406.19931","paper":"/paper/decoupling-general-and-personalized-knowledge","title":"Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition","date":"2024-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinghaowu/feddecomp","path":"models/Fed.py","file_url":"https://github.com/xinghaowu/feddecomp/blob/HEAD/models/Fed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e794f046561a69a","mcp_get_code":{"code_sha256":"9e794f046561a69a"}},{"arxiv_id":"2403.19211","paper":"/paper/dual-personalizing-adapter-for-federated","title":"Dual-Personalizing Adapter for Federated Foundation Models","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lydia-yang/FedDPA","path":"code/fed_utils/model_aggregation.py","file_url":"https://github.com/Lydia-yang/FedDPA/blob/HEAD/code/fed_utils/model_aggregation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5826791292da68e9","mcp_get_code":{"code_sha256":"5826791292da68e9"}},{"arxiv_id":"2311.13267","paper":"/paper/fedfn-feature-normalization-for-alleviating","title":"FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhoon-oh/FedBABU","path":"models/Fed.py","file_url":"https://github.com/jhoon-oh/FedBABU/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"2310.01542","paper":"/paper/fusing-models-with-complementary-expertise","title":"Fusing Models with Complementary Expertise","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hwang595/FoE-ICLR2024","path":"cifar10_non_iid_example.py","file_url":"https://github.com/hwang595/FoE-ICLR2024/blob/HEAD/cifar10_non_iid_example.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cddf6b6e878cc6ae","mcp_get_code":{"code_sha256":"cddf6b6e878cc6ae"}},{"arxiv_id":"2305.05644","paper":"/paper/towards-building-the-federated-gpt-federated","title":"Towards Building the Federated GPT: Federated Instruction Tuning","date":"2023-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jayzhang42/federatedgpt-shepherd","path":"fed_utils/model_aggregation.py","file_url":"https://github.com/jayzhang42/federatedgpt-shepherd/blob/HEAD/fed_utils/model_aggregation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e4ba8feaeb71a2ff","mcp_get_code":{"code_sha256":"e4ba8feaeb71a2ff"}},{"arxiv_id":"2209.00189","paper":"/paper/federated-learning-with-label-distribution","title":"Federated Learning with Label Distribution Skew via Logits Calibration","date":"2022-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/feddecorr","path":"approach/fedlogitcal.py","file_url":"https://github.com/bytedance/feddecorr/blob/HEAD/approach/fedlogitcal.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af8f3034a8ca7b6f","mcp_get_code":{"code_sha256":"af8f3034a8ca7b6f"}},{"arxiv_id":"2208.09754","paper":"/paper/flis-clustered-federated-learning-via","title":"FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution","date":"2022-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mmorafah/flis","path":"src/fedavg/fedavg.py","file_url":"https://github.com/mmorafah/flis/blob/HEAD/src/fedavg/fedavg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c42566650a9a2b17","mcp_get_code":{"code_sha256":"c42566650a9a2b17"}},{"arxiv_id":"2106.06042","paper":"/paper/fedbabu-towards-enhanced-representation-for","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhoon-oh/fedbabu","path":"models/Fed.py","file_url":"https://github.com/jhoon-oh/fedbabu/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"2010.02056","paper":"/paper/federated-learning-using-a-mixture-of-experts","title":"Specialized federated learning using a mixture of experts","date":"2020-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edvinli/federated-learning-mixture","path":"models/FederatedAveraging.py","file_url":"https://github.com/edvinli/federated-learning-mixture/blob/HEAD/models/FederatedAveraging.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"542a499a7eafaa04","mcp_get_code":{"code_sha256":"542a499a7eafaa04"}},{"arxiv_id":"2009.01974","paper":"/paper/feddistill-making-bayesian-model-ensemble","title":"FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning","date":"2020-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongyouc/FedBE","path":"models/Fed.py","file_url":"https://github.com/hongyouc/FedBE/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"276295afe2705ae2","mcp_get_code":{"code_sha256":"276295afe2705ae2"}},{"arxiv_id":"2004.12088","paper":"/paper/splitfed-when-federated-learning-meets-split","title":"SplitFed: When Federated Learning Meets Split Learning","date":"2020-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","path":"SFLV1_ResNet_HAM10000.py","file_url":"https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning/blob/HEAD/SFLV1_ResNet_HAM10000.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cddf6b6e878cc6ae","mcp_get_code":{"code_sha256":"cddf6b6e878cc6ae"}},{"arxiv_id":"2004.03657","paper":"/paper/fedmax-mitigating-activation-divergence-for","title":"FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning","date":"2020-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weichennone/FedMAX","path":"digit_object_recognition/models/Fed.py","file_url":"https://github.com/weichennone/FedMAX/blob/HEAD/digit_object_recognition/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"1907.01132","paper":"/paper/astraea-self-balancing-federated-learning-for","title":"Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications","date":"2019-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mtang724/Self-Balancing-Federated-Learning","path":"models/Fed.py","file_url":"https://github.com/mtang724/Self-Balancing-Federated-Learning/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"1812.07108","paper":"/paper/learning-private-neural-language-modeling","title":"Learning Private Neural Language Modeling with Attentive Aggregation","date":"2018-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shaoxiongji/federated-learning","path":"models/Fed.py","file_url":"https://github.com/shaoxiongji/federated-learning/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"1806.00582","paper":"/paper/federated-learning-with-non-iid-data","title":"Federated Learning with Non-IID Data","date":"2018-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjlee22/FedShare","path":"src/strategy.py","file_url":"https://github.com/yjlee22/FedShare/blob/HEAD/src/strategy.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75d8463105db0e97","mcp_get_code":{"code_sha256":"75d8463105db0e97"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peterhan91/thorax_gan","path":"Federated_GAN/models/Fed.py","file_url":"https://github.com/peterhan91/thorax_gan/blob/HEAD/Federated_GAN/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"75d8463105db0e97","mcp_get_code":{"code_sha256":"75d8463105db0e97"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongshenghu/source-inference-fl","path":"models/Fed.py","file_url":"https://github.com/hongshenghu/source-inference-fl/blob/HEAD/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f9964d719c5cb8f4","mcp_get_code":{"code_sha256":"f9964d719c5cb8f4"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HongshengHu/SIAs-Beyond_MIAs_in_Federated_Learning","path":"FedAvg-SIAs/models/Fed.py","file_url":"https://github.com/HongshengHu/SIAs-Beyond_MIAs_in_Federated_Learning/blob/HEAD/FedAvg-SIAs/models/Fed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5719ee7d03438fcb","mcp_get_code":{"code_sha256":"5719ee7d03438fcb"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carbonati/fl-zoo","path":"fl_zoo/optimizers/fed_avg.py","file_url":"https://github.com/carbonati/fl-zoo/blob/HEAD/fl_zoo/optimizers/fed_avg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ace0580d4c7a8494","mcp_get_code":{"code_sha256":"ace0580d4c7a8494"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TsingZ0/PFL-Non-IID","path":"system/flcore/servers/serveravg.py","file_url":"https://github.com/TsingZ0/PFL-Non-IID/blob/HEAD/system/flcore/servers/serveravg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"01b0d50f47833fc2","mcp_get_code":{"code_sha256":"01b0d50f47833fc2"}},{"arxiv_id":"1602.05629","paper":"/paper/communication-efficient-learning-of-deep","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","date":"2016-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thejungwon/gc-fed","path":"algorithms/fedavg.py","file_url":"https://github.com/thejungwon/gc-fed/blob/HEAD/algorithms/fedavg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"32845aa08d8061c2","mcp_get_code":{"code_sha256":"32845aa08d8061c2"}}]}