{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CCOFDHF2SAQBLVTRQ5IVFK5TLU","short_pith_number":"pith:CCOFDHF2","schema_version":"1.0","canonical_sha256":"109c519cba902015d671875152abb35d0aeab1ba3569bc016fbaf7a1d1df10a4","source":{"kind":"arxiv","id":"2409.18915","version":2},"attestation_state":"computed","paper":{"title":"A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dacheng Tao, Li Shen, Yan Sun","submitted_at":"2024-09-27T17:00:32Z","abstract_excerpt":"As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datasets on edged clients. Due to bandwidth limitations and security considerations, it ingeniously splits the original problem into multiple subproblems to be solved in parallel, which empowers primal dual solutions to great application values in FL. In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, whic"},"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":"2409.18915","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-27T17:00:32Z","cross_cats_sorted":[],"title_canon_sha256":"43ce9f95863ca6304a8e00355f001360d93b5aa5c24fc5e2f4d0eeba77a1c869","abstract_canon_sha256":"953e458077f7d5125f9ea5b3604628e20ea0d2af3e4ce857f8a3113b3afc699a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:52.628633Z","signature_b64":"fs+QF5HxlaZwTyJyuhySDXPTjMoU/k7fIT8zz7uFxM7TTDdeJf1gVttW309g56WPkA/TE8iEZLAV+6XVXJkECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"109c519cba902015d671875152abb35d0aeab1ba3569bc016fbaf7a1d1df10a4","last_reissued_at":"2026-07-05T10:02:52.628128Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:52.628128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dacheng Tao, Li Shen, Yan Sun","submitted_at":"2024-09-27T17:00:32Z","abstract_excerpt":"As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datasets on edged clients. Due to bandwidth limitations and security considerations, it ingeniously splits the original problem into multiple subproblems to be solved in parallel, which empowers primal dual solutions to great application values in FL. In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18915","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/2409.18915/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":"2409.18915","created_at":"2026-07-05T10:02:52.628191+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18915v2","created_at":"2026-07-05T10:02:52.628191+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18915","created_at":"2026-07-05T10:02:52.628191+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCOFDHF2SAQB","created_at":"2026-07-05T10:02:52.628191+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCOFDHF2SAQBLVTR","created_at":"2026-07-05T10:02:52.628191+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCOFDHF2","created_at":"2026-07-05T10:02:52.628191+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU","json":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU.json","graph_json":"https://pith.science/api/pith-number/CCOFDHF2SAQBLVTRQ5IVFK5TLU/graph.json","events_json":"https://pith.science/api/pith-number/CCOFDHF2SAQBLVTRQ5IVFK5TLU/events.json","paper":"https://pith.science/paper/CCOFDHF2"},"agent_actions":{"view_html":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU","download_json":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU.json","view_paper":"https://pith.science/paper/CCOFDHF2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18915&json=true","fetch_graph":"https://pith.science/api/pith-number/CCOFDHF2SAQBLVTRQ5IVFK5TLU/graph.json","fetch_events":"https://pith.science/api/pith-number/CCOFDHF2SAQBLVTRQ5IVFK5TLU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU/action/storage_attestation","attest_author":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU/action/author_attestation","sign_citation":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU/action/citation_signature","submit_replication":"https://pith.science/pith/CCOFDHF2SAQBLVTRQ5IVFK5TLU/action/replication_record"}},"created_at":"2026-07-05T10:02:52.628191+00:00","updated_at":"2026-07-05T10:02:52.628191+00:00"}