{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QRKBPSLX6VR72DAQSSQHCIFNOA","short_pith_number":"pith:QRKBPSLX","schema_version":"1.0","canonical_sha256":"845417c977f563fd0c1094a07120ad7037f400fb5f92f8f3160bb1d08c173070","source":{"kind":"arxiv","id":"2312.04992","version":2},"attestation_state":"computed","paper":{"title":"PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Hao Wang, Jian Cao, Jianqing Zhang, Ruhui Ma, Tao Song, Yang Hua, Yang Liu, Zhengui Xue","submitted_at":"2023-12-08T12:03:08Z","abstract_excerpt":"Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL)has gained significant prominence as a research direction within the FL domain. Whereas traditional FL (tFL) focuses on jointly learning a global model, pFL aims to balance each client's global and personalized goals in FL settings. To foster the pFL research community, we started and built PFLlib, a comprehensive pFL library with an integrated benchmark platform. In PFLlib, we implemented 37 state-of-the-art FL algorithms ("},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.04992","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-08T12:03:08Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"c8f68c96e1252f4f1682d8030d4c33c2ae8c413a9096d7838acf09d72fdba40f","abstract_canon_sha256":"cc6d490514605443e8a28dc0046827fb4a2493982916dc47baabc980c3bc3725"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:35.092761Z","signature_b64":"FmURaxOhZQ5uesEFJkmhEB04i5O+/W81jgLrnBsVeI1nchXnAWzBGt+UhH5y/Y+nawU1OvRk2AkxzrH2dOKzAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"845417c977f563fd0c1094a07120ad7037f400fb5f92f8f3160bb1d08c173070","last_reissued_at":"2026-07-05T10:20:35.092125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:35.092125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Hao Wang, Jian Cao, Jianqing Zhang, Ruhui Ma, Tao Song, Yang Hua, Yang Liu, Zhengui Xue","submitted_at":"2023-12-08T12:03:08Z","abstract_excerpt":"Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL)has gained significant prominence as a research direction within the FL domain. Whereas traditional FL (tFL) focuses on jointly learning a global model, pFL aims to balance each client's global and personalized goals in FL settings. To foster the pFL research community, we started and built PFLlib, a comprehensive pFL library with an integrated benchmark platform. In PFLlib, we implemented 37 state-of-the-art FL algorithms ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04992","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.04992/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.04992","created_at":"2026-07-05T10:20:35.092198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.04992v2","created_at":"2026-07-05T10:20:35.092198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04992","created_at":"2026-07-05T10:20:35.092198+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRKBPSLX6VR7","created_at":"2026-07-05T10:20:35.092198+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRKBPSLX6VR72DAQ","created_at":"2026-07-05T10:20:35.092198+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRKBPSLX","created_at":"2026-07-05T10:20:35.092198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17373","citing_title":"FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2405.16240","citing_title":"AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17373","citing_title":"FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics","ref_index":63,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA","json":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA.json","graph_json":"https://pith.science/api/pith-number/QRKBPSLX6VR72DAQSSQHCIFNOA/graph.json","events_json":"https://pith.science/api/pith-number/QRKBPSLX6VR72DAQSSQHCIFNOA/events.json","paper":"https://pith.science/paper/QRKBPSLX"},"agent_actions":{"view_html":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA","download_json":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA.json","view_paper":"https://pith.science/paper/QRKBPSLX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.04992&json=true","fetch_graph":"https://pith.science/api/pith-number/QRKBPSLX6VR72DAQSSQHCIFNOA/graph.json","fetch_events":"https://pith.science/api/pith-number/QRKBPSLX6VR72DAQSSQHCIFNOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA/action/storage_attestation","attest_author":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA/action/author_attestation","sign_citation":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA/action/citation_signature","submit_replication":"https://pith.science/pith/QRKBPSLX6VR72DAQSSQHCIFNOA/action/replication_record"}},"created_at":"2026-07-05T10:20:35.092198+00:00","updated_at":"2026-07-05T10:20:35.092198+00:00"}