{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BATTFVAL5X2Y72M7EECPY76XTS","short_pith_number":"pith:BATTFVAL","schema_version":"1.0","canonical_sha256":"082732d40bedf58fe99f2104fc7fd79cb7770d7692407151fd849a0c68477ca2","source":{"kind":"arxiv","id":"2308.12681","version":2},"attestation_state":"computed","paper":{"title":"LR-XFL: Logical Reasoning-based Explainable Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Han Yu, Yanci Zhang","submitted_at":"2023-08-24T09:40:37Z","abstract_excerpt":"Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules throu"},"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":"2308.12681","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-08-24T09:40:37Z","cross_cats_sorted":["cs.DC","cs.LG","cs.LO"],"title_canon_sha256":"88deb4438a1683675e5d9b140a4728e2c505d0b52e697d7f6328f1c0d4c9c4dc","abstract_canon_sha256":"102ae4b98c3171590889a0cabcf63cde989b32d2bd1d41649648648fed11c422"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:25:44.720409Z","signature_b64":"c4tjpyw0awnWXlc7IRCQFH/DBSrtugoDKgff23hByjkrX4QbiZJRmjGkDGLtzgRlxga7zO/GUdqkeJ1lXNbfAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"082732d40bedf58fe99f2104fc7fd79cb7770d7692407151fd849a0c68477ca2","last_reissued_at":"2026-07-05T07:25:44.719931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:25:44.719931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LR-XFL: Logical Reasoning-based Explainable Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Han Yu, Yanci Zhang","submitted_at":"2023-08-24T09:40:37Z","abstract_excerpt":"Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules throu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12681","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/2308.12681/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":"2308.12681","created_at":"2026-07-05T07:25:44.719988+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.12681v2","created_at":"2026-07-05T07:25:44.719988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12681","created_at":"2026-07-05T07:25:44.719988+00:00"},{"alias_kind":"pith_short_12","alias_value":"BATTFVAL5X2Y","created_at":"2026-07-05T07:25:44.719988+00:00"},{"alias_kind":"pith_short_16","alias_value":"BATTFVAL5X2Y72M7","created_at":"2026-07-05T07:25:44.719988+00:00"},{"alias_kind":"pith_short_8","alias_value":"BATTFVAL","created_at":"2026-07-05T07:25:44.719988+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/BATTFVAL5X2Y72M7EECPY76XTS","json":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS.json","graph_json":"https://pith.science/api/pith-number/BATTFVAL5X2Y72M7EECPY76XTS/graph.json","events_json":"https://pith.science/api/pith-number/BATTFVAL5X2Y72M7EECPY76XTS/events.json","paper":"https://pith.science/paper/BATTFVAL"},"agent_actions":{"view_html":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS","download_json":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS.json","view_paper":"https://pith.science/paper/BATTFVAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.12681&json=true","fetch_graph":"https://pith.science/api/pith-number/BATTFVAL5X2Y72M7EECPY76XTS/graph.json","fetch_events":"https://pith.science/api/pith-number/BATTFVAL5X2Y72M7EECPY76XTS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS/action/storage_attestation","attest_author":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS/action/author_attestation","sign_citation":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS/action/citation_signature","submit_replication":"https://pith.science/pith/BATTFVAL5X2Y72M7EECPY76XTS/action/replication_record"}},"created_at":"2026-07-05T07:25:44.719988+00:00","updated_at":"2026-07-05T07:25:44.719988+00:00"}