{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UB4LFBBMYNYFI4C2EXHUOH2DM3","short_pith_number":"pith:UB4LFBBM","schema_version":"1.0","canonical_sha256":"a078b2842cc37054705a25cf471f4366d59d88e852c3d628db2c5a42cdb32a3b","source":{"kind":"arxiv","id":"2411.13740","version":1},"attestation_state":"computed","paper":{"title":"Federated Continual Learning for Edge-AI: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.NI"],"primary_cat":"cs.LG","authors_text":"Fei Wu, Feng Yu, Geyong Min, Jia Hu, Yurui Zhou, Zi Wang","submitted_at":"2024-11-20T22:49:28Z","abstract_excerpt":"Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-t"},"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":"2411.13740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T22:49:28Z","cross_cats_sorted":["cs.AI","cs.DC","cs.NI"],"title_canon_sha256":"37dceda1c3cf0f0f94bf36febc6dddd997e196f81ba806bf22e8ad1adb5e8e9e","abstract_canon_sha256":"e2b77166af99e9ba32821a4d342b067bbc188fcc2f5635380eaae8c750c7dc07"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:36.523839Z","signature_b64":"/5Z63juXpOdo/YRSMzv/+/k4JhXq1PlPWKntA20fS3b8eydko5TVCDiSOQ0N1oGPbySxGLW+Ha7+PrU8u5KhAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a078b2842cc37054705a25cf471f4366d59d88e852c3d628db2c5a42cdb32a3b","last_reissued_at":"2026-07-05T09:38:36.523241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:36.523241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Continual Learning for Edge-AI: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.NI"],"primary_cat":"cs.LG","authors_text":"Fei Wu, Feng Yu, Geyong Min, Jia Hu, Yurui Zhou, Zi Wang","submitted_at":"2024-11-20T22:49:28Z","abstract_excerpt":"Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13740","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/2411.13740/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":"2411.13740","created_at":"2026-07-05T09:38:36.523303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.13740v1","created_at":"2026-07-05T09:38:36.523303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13740","created_at":"2026-07-05T09:38:36.523303+00:00"},{"alias_kind":"pith_short_12","alias_value":"UB4LFBBMYNYF","created_at":"2026-07-05T09:38:36.523303+00:00"},{"alias_kind":"pith_short_16","alias_value":"UB4LFBBMYNYFI4C2","created_at":"2026-07-05T09:38:36.523303+00:00"},{"alias_kind":"pith_short_8","alias_value":"UB4LFBBM","created_at":"2026-07-05T09:38:36.523303+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11272","citing_title":"Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19145","citing_title":"PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2505.12318","citing_title":"Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19145","citing_title":"PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3","json":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3.json","graph_json":"https://pith.science/api/pith-number/UB4LFBBMYNYFI4C2EXHUOH2DM3/graph.json","events_json":"https://pith.science/api/pith-number/UB4LFBBMYNYFI4C2EXHUOH2DM3/events.json","paper":"https://pith.science/paper/UB4LFBBM"},"agent_actions":{"view_html":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3","download_json":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3.json","view_paper":"https://pith.science/paper/UB4LFBBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.13740&json=true","fetch_graph":"https://pith.science/api/pith-number/UB4LFBBMYNYFI4C2EXHUOH2DM3/graph.json","fetch_events":"https://pith.science/api/pith-number/UB4LFBBMYNYFI4C2EXHUOH2DM3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3/action/storage_attestation","attest_author":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3/action/author_attestation","sign_citation":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3/action/citation_signature","submit_replication":"https://pith.science/pith/UB4LFBBMYNYFI4C2EXHUOH2DM3/action/replication_record"}},"created_at":"2026-07-05T09:38:36.523303+00:00","updated_at":"2026-07-05T09:38:36.523303+00:00"}