{"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/localupdate","entry":"LocalUpdate","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":7,"n_papers_ran":4,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":3},"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":"2407.03247","paper":"/paper/bridging-model-heterogeneity-in-federated","title":"Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning","date":null,"month_inferred_from_arxiv_id":"2024-07","title_source":"archive","repo":"JackqqWang/FedType","path":"update_new.py","file_url":"https://github.com/JackqqWang/FedType/blob/HEAD/update_new.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b8e2d199f75230eb","mcp_get_code":{"code_sha256":"b8e2d199f75230eb"}},{"arxiv_id":"2305.05230","paper":"/paper/fednoro-towards-noise-robust-federated","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","date":"2023-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wnn2000/fednoro","path":"utils/local_training.py","file_url":"https://github.com/wnn2000/fednoro/blob/HEAD/utils/local_training.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e50ca4f537d029c","mcp_get_code":{"code_sha256":"6e50ca4f537d029c"}},{"arxiv_id":"2303.00250","paper":"/paper/combating-exacerbated-heterogeneity-for","title":"Combating Exacerbated Heterogeneity for Robust Models in Federated Learning","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zfancy/sfat","path":"update.py","file_url":"https://github.com/zfancy/sfat/blob/HEAD/update.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cafa55b9accbd330","mcp_get_code":{"code_sha256":"cafa55b9accbd330"}},{"arxiv_id":"2207.06936","paper":"/paper/multi-level-branched-regularization-for","title":"Multi-Level Branched Regularization for Federated Learning","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinkyu032/FedMLB","path":"local_update_method/FedMLB.py","file_url":"https://github.com/jinkyu032/FedMLB/blob/HEAD/local_update_method/FedMLB.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"40884a90f0090917","mcp_get_code":{"code_sha256":"40884a90f0090917"}},{"arxiv_id":"2204.04677","paper":"/paper/fedcorr-multi-stage-federated-learning-for","title":"FedCorr: Multi-Stage Federated Learning for Label Noise Correction","date":"2022-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xu-jingyi/fedcorr","path":"util/local_training.py","file_url":"https://github.com/xu-jingyi/fedcorr/blob/HEAD/util/local_training.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ba9a691850ab6c1c","mcp_get_code":{"code_sha256":"ba9a691850ab6c1c"}},{"arxiv_id":"2110.13864","paper":"/paper/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","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeremy313/fl-wbc","path":"src/update.py","file_url":"https://github.com/jeremy313/fl-wbc/blob/HEAD/src/update.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f147694b5e3057d9","mcp_get_code":{"code_sha256":"f147694b5e3057d9"}},{"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":"yuetan031/fedproto","path":"lib/update.py","file_url":"https://github.com/yuetan031/fedproto/blob/HEAD/lib/update.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cbaf694f21e1aee7","mcp_get_code":{"code_sha256":"cbaf694f21e1aee7"}}]}