{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E2BTZDMTC4GUSB6PAG7MD7NM4V","short_pith_number":"pith:E2BTZDMT","schema_version":"1.0","canonical_sha256":"26833c8d93170d4907cf01bec1fdace5652b1789ef041f5c77a91429b1a3e667","source":{"kind":"arxiv","id":"2505.04873","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Christopher G. Brinton, David J. Love, Dinh C. Nguyen, H. Vincent Poor, Mayuri Wijayasundara, Minh K. Quan, Pubudu N. Pathirana, Sujeeva Setunge","submitted_at":"2025-05-08T01:17:15Z","abstract_excerpt":"The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer,"},"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":"2505.04873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-08T01:17:15Z","cross_cats_sorted":["cs.AI","cs.CR","cs.DC"],"title_canon_sha256":"3a8819876e06bea92f8669d9eb11e4ef052aed00a16b4d827d68a40c3b8f1a94","abstract_canon_sha256":"724296492f2c3d37233831ee7e87ff0428208a38953dfd1b50a9ab05f89342fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:22.211738Z","signature_b64":"GoBKOb2n+a8TH6IFjIe1MDaPwD+LhKFyf3ZoG0MPgZRg5cCQuIQOetmKtd6dgChTXJfa3HHtXzxfknpCFm29Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26833c8d93170d4907cf01bec1fdace5652b1789ef041f5c77a91429b1a3e667","last_reissued_at":"2026-07-05T11:04:22.211236Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:22.211236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Christopher G. Brinton, David J. Love, Dinh C. Nguyen, H. Vincent Poor, Mayuri Wijayasundara, Minh K. Quan, Pubudu N. Pathirana, Sujeeva Setunge","submitted_at":"2025-05-08T01:17:15Z","abstract_excerpt":"The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04873","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/2505.04873/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":"2505.04873","created_at":"2026-07-05T11:04:22.211300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04873v1","created_at":"2026-07-05T11:04:22.211300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04873","created_at":"2026-07-05T11:04:22.211300+00:00"},{"alias_kind":"pith_short_12","alias_value":"E2BTZDMTC4GU","created_at":"2026-07-05T11:04:22.211300+00:00"},{"alias_kind":"pith_short_16","alias_value":"E2BTZDMTC4GUSB6P","created_at":"2026-07-05T11:04:22.211300+00:00"},{"alias_kind":"pith_short_8","alias_value":"E2BTZDMT","created_at":"2026-07-05T11:04:22.211300+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27674","citing_title":"Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V","json":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V.json","graph_json":"https://pith.science/api/pith-number/E2BTZDMTC4GUSB6PAG7MD7NM4V/graph.json","events_json":"https://pith.science/api/pith-number/E2BTZDMTC4GUSB6PAG7MD7NM4V/events.json","paper":"https://pith.science/paper/E2BTZDMT"},"agent_actions":{"view_html":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V","download_json":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V.json","view_paper":"https://pith.science/paper/E2BTZDMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04873&json=true","fetch_graph":"https://pith.science/api/pith-number/E2BTZDMTC4GUSB6PAG7MD7NM4V/graph.json","fetch_events":"https://pith.science/api/pith-number/E2BTZDMTC4GUSB6PAG7MD7NM4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V/action/storage_attestation","attest_author":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V/action/author_attestation","sign_citation":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V/action/citation_signature","submit_replication":"https://pith.science/pith/E2BTZDMTC4GUSB6PAG7MD7NM4V/action/replication_record"}},"created_at":"2026-07-05T11:04:22.211300+00:00","updated_at":"2026-07-05T11:04:22.211300+00:00"}