{"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/build-non-iid-by-dirichlet","entry":"build_non_iid_by_dirichlet","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+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":5,"n_papers_ran":5,"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":5,"n_samples_ran":5,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":3,"unverified":0},"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":"2605.13475","paper":"/paper/arxiv-2605-13475","title":"FedHPro: Federated Hyper-Prototype Learning via Gradient Matching","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"mala-lab/FedHPro","path":"main_run.py","file_url":"https://github.com/mala-lab/FedHPro/blob/HEAD/main_run.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b4c1b12e55eb5ab1","mcp_get_code":{"code_sha256":"b4c1b12e55eb5ab1"}},{"arxiv_id":"2410.12926","paper":"/paper/deer-deviation-eliminating-and-noise","title":"DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cuhk-aim-group/deer","path":"datasets.py","file_url":"https://github.com/cuhk-aim-group/deer/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"747d0b39f329a800","mcp_get_code":{"code_sha256":"747d0b39f329a800"}},{"arxiv_id":"2409.12105","paper":"/paper/fedlf-adaptive-logit-adjustment-and-feature","title":"FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"18sym/FedLF","path":"algorithm/fedlf.py","file_url":"https://github.com/18sym/FedLF/blob/HEAD/algorithm/fedlf.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2ff16fc9fa3fcdcb","mcp_get_code":{"code_sha256":"2ff16fc9fa3fcdcb"}},{"arxiv_id":"2302.13485","paper":"/paper/fedclip-fast-generalization-and","title":"FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning","date":"2023-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/personalizedfl","path":"datautil/datasplit.py","file_url":"https://github.com/microsoft/personalizedfl/blob/HEAD/datautil/datasplit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f74d575af34e74e7","mcp_get_code":{"code_sha256":"f74d575af34e74e7"}},{"arxiv_id":"2205.10920","paper":"/paper/test-time-robust-personalization-for","title":"Test-Time Robust Personalization for Federated Learning","date":"2022-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LINs-lab/FedTHE","path":"BRFL/partition_data.py","file_url":"https://github.com/LINs-lab/FedTHE/blob/HEAD/BRFL/partition_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"23af7c10d53271dd","mcp_get_code":{"code_sha256":"23af7c10d53271dd"}}]}