{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:P6VDDCZVWG2AMO7QXR2FN4SHW4","short_pith_number":"pith:P6VDDCZV","schema_version":"1.0","canonical_sha256":"7faa318b35b1b4063bf0bc7456f247b731220315b292fbe2d43b00e3e7c9d822","source":{"kind":"arxiv","id":"2505.23849","version":2},"attestation_state":"computed","paper":{"title":"CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aditya Sinha, Kaveen Hiniduma, Ravi Madduri, Suren Byna, Zilinghan Li","submitted_at":"2025-05-28T21:24:46Z","abstract_excerpt":"Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific"},"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":"2505.23849","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-05-28T21:24:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"e9a7c5afe2d4254c458f0c793bf4f02fee14fc2af230c94b728760291d0a918a","abstract_canon_sha256":"8ded574ec0340c922b8f57e8c482c0efc8be524a1f1a8173dc77363216bb85ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:02.450852Z","signature_b64":"Somv3rTkwmIdoU2utA4GzpFEUVEZNASv0yvSF4DtPFSApVVlLHGFt2zUh8VS7GyGelV3a6j4X8B4YGocv6dnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7faa318b35b1b4063bf0bc7456f247b731220315b292fbe2d43b00e3e7c9d822","last_reissued_at":"2026-07-05T11:52:02.450370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:02.450370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aditya Sinha, Kaveen Hiniduma, Ravi Madduri, Suren Byna, Zilinghan Li","submitted_at":"2025-05-28T21:24:46Z","abstract_excerpt":"Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23849","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/2505.23849/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":"2505.23849","created_at":"2026-07-05T11:52:02.450428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.23849v2","created_at":"2026-07-05T11:52:02.450428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23849","created_at":"2026-07-05T11:52:02.450428+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6VDDCZVWG2A","created_at":"2026-07-05T11:52:02.450428+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6VDDCZVWG2AMO7Q","created_at":"2026-07-05T11:52:02.450428+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6VDDCZV","created_at":"2026-07-05T11:52:02.450428+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/P6VDDCZVWG2AMO7QXR2FN4SHW4","json":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4.json","graph_json":"https://pith.science/api/pith-number/P6VDDCZVWG2AMO7QXR2FN4SHW4/graph.json","events_json":"https://pith.science/api/pith-number/P6VDDCZVWG2AMO7QXR2FN4SHW4/events.json","paper":"https://pith.science/paper/P6VDDCZV"},"agent_actions":{"view_html":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4","download_json":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4.json","view_paper":"https://pith.science/paper/P6VDDCZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.23849&json=true","fetch_graph":"https://pith.science/api/pith-number/P6VDDCZVWG2AMO7QXR2FN4SHW4/graph.json","fetch_events":"https://pith.science/api/pith-number/P6VDDCZVWG2AMO7QXR2FN4SHW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4/action/storage_attestation","attest_author":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4/action/author_attestation","sign_citation":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4/action/citation_signature","submit_replication":"https://pith.science/pith/P6VDDCZVWG2AMO7QXR2FN4SHW4/action/replication_record"}},"created_at":"2026-07-05T11:52:02.450428+00:00","updated_at":"2026-07-05T11:52:02.450428+00:00"}