{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7QJZP6MOQQXN5OJ4LIW2QFNNKW","short_pith_number":"pith:7QJZP6MO","schema_version":"1.0","canonical_sha256":"fc1397f98e842edeb93c5a2da815ad55a4c9ec7f68b12790c4963abef9aed12e","source":{"kind":"arxiv","id":"2305.09134","version":2},"attestation_state":"computed","paper":{"title":"Smart Policy Control for Securing Federated Learning Management System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman","submitted_at":"2023-05-16T03:34:49Z","abstract_excerpt":"The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL "},"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":"2305.09134","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2023-05-16T03:34:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1d64bbe2d9abcbfaa17085db097559eb7b9ea161b0595e5bd6653217dcbead26","abstract_canon_sha256":"39ef8011a78d3e468481a576f5c4f9b048633956cf89401f0281ff783ba883d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:11:21.493138Z","signature_b64":"6MoBhAnFJvXLHbpKhMDtDLkU5hCvuMstPHlQHqWfotmAiz4QzdxnzdoM5yALqYzENWb5zOdTGpbJn9HzLhxsDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc1397f98e842edeb93c5a2da815ad55a4c9ec7f68b12790c4963abef9aed12e","last_reissued_at":"2026-07-05T06:11:21.492734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:11:21.492734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Smart Policy Control for Securing Federated Learning Management System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman","submitted_at":"2023-05-16T03:34:49Z","abstract_excerpt":"The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09134","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/2305.09134/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":"2305.09134","created_at":"2026-07-05T06:11:21.492794+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.09134v2","created_at":"2026-07-05T06:11:21.492794+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09134","created_at":"2026-07-05T06:11:21.492794+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QJZP6MOQQXN","created_at":"2026-07-05T06:11:21.492794+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QJZP6MOQQXN5OJ4","created_at":"2026-07-05T06:11:21.492794+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QJZP6MO","created_at":"2026-07-05T06:11:21.492794+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.00091","citing_title":"Graph Canvas for Controllable 3D Scene Generation","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW","json":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW.json","graph_json":"https://pith.science/api/pith-number/7QJZP6MOQQXN5OJ4LIW2QFNNKW/graph.json","events_json":"https://pith.science/api/pith-number/7QJZP6MOQQXN5OJ4LIW2QFNNKW/events.json","paper":"https://pith.science/paper/7QJZP6MO"},"agent_actions":{"view_html":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW","download_json":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW.json","view_paper":"https://pith.science/paper/7QJZP6MO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.09134&json=true","fetch_graph":"https://pith.science/api/pith-number/7QJZP6MOQQXN5OJ4LIW2QFNNKW/graph.json","fetch_events":"https://pith.science/api/pith-number/7QJZP6MOQQXN5OJ4LIW2QFNNKW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW/action/storage_attestation","attest_author":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW/action/author_attestation","sign_citation":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW/action/citation_signature","submit_replication":"https://pith.science/pith/7QJZP6MOQQXN5OJ4LIW2QFNNKW/action/replication_record"}},"created_at":"2026-07-05T06:11:21.492794+00:00","updated_at":"2026-07-05T06:11:21.492794+00:00"}