{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PCCOPIGALELRQURP6SA6TBVP25","short_pith_number":"pith:PCCOPIGA","schema_version":"1.0","canonical_sha256":"7884e7a0c0591718522ff481e986afd772be0ce0cb32cf3282e9500204ed808e","source":{"kind":"arxiv","id":"2302.13473","version":2},"attestation_state":"computed","paper":{"title":"Towards Interpretable Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anran Li, Han Yu, Lizhen Cui, Ming Hu, Rui Liu, Shipeng Wang, Yuanyuan Chen","submitted_at":"2023-02-27T02:06:18Z","abstract_excerpt":"Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary nature can be challenging for new researchers to pick"},"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":"2302.13473","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-27T02:06:18Z","cross_cats_sorted":[],"title_canon_sha256":"319bb46ad0365b602c9fd0e5e4ee5327554c098fff60d94d30c098680a565584","abstract_canon_sha256":"4f27ba719565920bbc94af16664bb329637bd0acdbc49107517097975bf5cade"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:05:31.619982Z","signature_b64":"siufB/5xTOF0gXnpsblNigXhW0YvZjk+ybZVM61CowGtLUYhXcloRbmVkDXh4cQOmnILze1bV/ywQqG+3BksBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7884e7a0c0591718522ff481e986afd772be0ce0cb32cf3282e9500204ed808e","last_reissued_at":"2026-05-27T01:05:31.619352Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:05:31.619352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Interpretable Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anran Li, Han Yu, Lizhen Cui, Ming Hu, Rui Liu, Shipeng Wang, Yuanyuan Chen","submitted_at":"2023-02-27T02:06:18Z","abstract_excerpt":"Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary nature can be challenging for new researchers to pick"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13473","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/2302.13473/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":"2302.13473","created_at":"2026-05-27T01:05:31.619427+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.13473v2","created_at":"2026-05-27T01:05:31.619427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13473","created_at":"2026-05-27T01:05:31.619427+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCCOPIGALELR","created_at":"2026-05-27T01:05:31.619427+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCCOPIGALELRQURP","created_at":"2026-05-27T01:05:31.619427+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCCOPIGA","created_at":"2026-05-27T01:05:31.619427+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.17956","citing_title":"Federated Rule Ensemble Method in Medical Data","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25","json":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25.json","graph_json":"https://pith.science/api/pith-number/PCCOPIGALELRQURP6SA6TBVP25/graph.json","events_json":"https://pith.science/api/pith-number/PCCOPIGALELRQURP6SA6TBVP25/events.json","paper":"https://pith.science/paper/PCCOPIGA"},"agent_actions":{"view_html":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25","download_json":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25.json","view_paper":"https://pith.science/paper/PCCOPIGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.13473&json=true","fetch_graph":"https://pith.science/api/pith-number/PCCOPIGALELRQURP6SA6TBVP25/graph.json","fetch_events":"https://pith.science/api/pith-number/PCCOPIGALELRQURP6SA6TBVP25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25/action/storage_attestation","attest_author":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25/action/author_attestation","sign_citation":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25/action/citation_signature","submit_replication":"https://pith.science/pith/PCCOPIGALELRQURP6SA6TBVP25/action/replication_record"}},"created_at":"2026-05-27T01:05:31.619427+00:00","updated_at":"2026-05-27T01:05:31.619427+00:00"}