{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EGXYD7IXLNQWBTI3PIKYTFMC7V","short_pith_number":"pith:EGXYD7IX","schema_version":"1.0","canonical_sha256":"21af81fd175b6160cd1b7a15899582fd5a12e720e2e40a5be8cf8899b7ba2df4","source":{"kind":"arxiv","id":"2306.17465","version":1},"attestation_state":"computed","paper":{"title":"FedBone: Towards Large-Scale Federated Multi-Task Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenlong Gao, Qian Chen, Teng Zhang, Wuliang Huang, Xinlong Jiang, Yiqiang Chen","submitted_at":"2023-06-30T08:19:38Z","abstract_excerpt":"Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive predictions. In real-world applications, visual and natural language tasks typically require large-scale models to extract high-level abstract features. However, large-scale models cannot be directly applied to existing federated multi-task learning methods. Existing HFML methods also disregard the impact of gradient conflicts on multi-task optimization during the federated aggregation process. In this work, we prop"},"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":"2306.17465","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T08:19:38Z","cross_cats_sorted":[],"title_canon_sha256":"8e1464b978242ec7187ad0064e62cd502c7d4d18e36042346576af720308d32c","abstract_canon_sha256":"be6d94dd924cfa67ff176437f81097898ab9491a5c768cdaab2682b4c1801e73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:27.510926Z","signature_b64":"U3dfRqC34I7ecz+/H4ib6LIwEW/gJEKoj119riqfTlxgguYviRycdJA3W8TpzR8yzRDB0zFDAeL+gblOuhHpCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21af81fd175b6160cd1b7a15899582fd5a12e720e2e40a5be8cf8899b7ba2df4","last_reissued_at":"2026-07-05T06:26:27.510479Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:27.510479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FedBone: Towards Large-Scale Federated Multi-Task Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenlong Gao, Qian Chen, Teng Zhang, Wuliang Huang, Xinlong Jiang, Yiqiang Chen","submitted_at":"2023-06-30T08:19:38Z","abstract_excerpt":"Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive predictions. In real-world applications, visual and natural language tasks typically require large-scale models to extract high-level abstract features. However, large-scale models cannot be directly applied to existing federated multi-task learning methods. Existing HFML methods also disregard the impact of gradient conflicts on multi-task optimization during the federated aggregation process. In this work, we prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.17465","kind":"arxiv","version":1},"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/2306.17465/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":"2306.17465","created_at":"2026-07-05T06:26:27.510549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.17465v1","created_at":"2026-07-05T06:26:27.510549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.17465","created_at":"2026-07-05T06:26:27.510549+00:00"},{"alias_kind":"pith_short_12","alias_value":"EGXYD7IXLNQW","created_at":"2026-07-05T06:26:27.510549+00:00"},{"alias_kind":"pith_short_16","alias_value":"EGXYD7IXLNQWBTI3","created_at":"2026-07-05T06:26:27.510549+00:00"},{"alias_kind":"pith_short_8","alias_value":"EGXYD7IX","created_at":"2026-07-05T06:26:27.510549+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.06376","citing_title":"Many-Task Federated Fine-Tuning via Unified Task Vectors","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V","json":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V.json","graph_json":"https://pith.science/api/pith-number/EGXYD7IXLNQWBTI3PIKYTFMC7V/graph.json","events_json":"https://pith.science/api/pith-number/EGXYD7IXLNQWBTI3PIKYTFMC7V/events.json","paper":"https://pith.science/paper/EGXYD7IX"},"agent_actions":{"view_html":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V","download_json":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V.json","view_paper":"https://pith.science/paper/EGXYD7IX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.17465&json=true","fetch_graph":"https://pith.science/api/pith-number/EGXYD7IXLNQWBTI3PIKYTFMC7V/graph.json","fetch_events":"https://pith.science/api/pith-number/EGXYD7IXLNQWBTI3PIKYTFMC7V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V/action/storage_attestation","attest_author":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V/action/author_attestation","sign_citation":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V/action/citation_signature","submit_replication":"https://pith.science/pith/EGXYD7IXLNQWBTI3PIKYTFMC7V/action/replication_record"}},"created_at":"2026-07-05T06:26:27.510549+00:00","updated_at":"2026-07-05T06:26:27.510549+00:00"}