{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VGZRMRBGDOLMYUEWTLPK73HLJI","short_pith_number":"pith:VGZRMRBG","schema_version":"1.0","canonical_sha256":"a9b31644261b96cc50969adeafeceb4a2c4a95b76a4d2c15e31dda5c63b7efdf","source":{"kind":"arxiv","id":"2211.12049","version":1},"attestation_state":"computed","paper":{"title":"GitFL: Adaptive Asynchronous Federated Learning using Version Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jun Xia, Ming Hu, Mingsong Chen, Yang Liu, Yihao Huang, Zeke Xia, Zhihao Yue","submitted_at":"2022-11-22T06:45:40Z","abstract_excerpt":"As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issu"},"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":"2211.12049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-22T06:45:40Z","cross_cats_sorted":[],"title_canon_sha256":"d53f3650f77aceef9dfe4dc0c89d3307f0afb4046eaea744b771a1f2071d2465","abstract_canon_sha256":"89b50905dee0f05481c5abfd344570a14f1c83adf4d312f93e4a0bc3163be7ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:26.314344Z","signature_b64":"ctavszse8owFuXBLa/SN5AcJon/n7MHvY9YlSRtVDezjfLYmdYcykHdoTx84ucGbnf5fmmGp5RjcAu7J2pc5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9b31644261b96cc50969adeafeceb4a2c4a95b76a4d2c15e31dda5c63b7efdf","last_reissued_at":"2026-07-05T07:31:26.313779Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:26.313779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GitFL: Adaptive Asynchronous Federated Learning using Version Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jun Xia, Ming Hu, Mingsong Chen, Yang Liu, Yihao Huang, Zeke Xia, Zhihao Yue","submitted_at":"2022-11-22T06:45:40Z","abstract_excerpt":"As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12049","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/2211.12049/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":"2211.12049","created_at":"2026-07-05T07:31:26.313839+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.12049v1","created_at":"2026-07-05T07:31:26.313839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12049","created_at":"2026-07-05T07:31:26.313839+00:00"},{"alias_kind":"pith_short_12","alias_value":"VGZRMRBGDOLM","created_at":"2026-07-05T07:31:26.313839+00:00"},{"alias_kind":"pith_short_16","alias_value":"VGZRMRBGDOLMYUEW","created_at":"2026-07-05T07:31:26.313839+00:00"},{"alias_kind":"pith_short_8","alias_value":"VGZRMRBG","created_at":"2026-07-05T07:31:26.313839+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/VGZRMRBGDOLMYUEWTLPK73HLJI","json":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI.json","graph_json":"https://pith.science/api/pith-number/VGZRMRBGDOLMYUEWTLPK73HLJI/graph.json","events_json":"https://pith.science/api/pith-number/VGZRMRBGDOLMYUEWTLPK73HLJI/events.json","paper":"https://pith.science/paper/VGZRMRBG"},"agent_actions":{"view_html":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI","download_json":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI.json","view_paper":"https://pith.science/paper/VGZRMRBG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.12049&json=true","fetch_graph":"https://pith.science/api/pith-number/VGZRMRBGDOLMYUEWTLPK73HLJI/graph.json","fetch_events":"https://pith.science/api/pith-number/VGZRMRBGDOLMYUEWTLPK73HLJI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI/action/storage_attestation","attest_author":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI/action/author_attestation","sign_citation":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI/action/citation_signature","submit_replication":"https://pith.science/pith/VGZRMRBGDOLMYUEWTLPK73HLJI/action/replication_record"}},"created_at":"2026-07-05T07:31:26.313839+00:00","updated_at":"2026-07-05T07:31:26.313839+00:00"}