{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IAXM2VHW54FGDRNIXDZXHYABQK","short_pith_number":"pith:IAXM2VHW","schema_version":"1.0","canonical_sha256":"402ecd54f6ef0a61c5a8b8f373e00182ae5697451c6b79224a2c94311c02f342","source":{"kind":"arxiv","id":"2501.06953","version":1},"attestation_state":"computed","paper":{"title":"ByzSFL: Achieving Byzantine-Robust Secure Federated Learning with Zero-Knowledge Proofs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chenghong Wang, Haixu Tang, Hyunghoon Cho, Lucila Ohno-Machado, Rui Zhu, Ye Dong, Yongming Fan, Zihao Wang","submitted_at":"2025-01-12T22:14:45Z","abstract_excerpt":"The advancement of AI models, especially those powered by deep learning, faces significant challenges in data-sensitive industries like healthcare and finance due to the distributed and private nature of data. Federated Learning (FL) and Secure Federated Learning (SFL) enable collaborative model training without data sharing, enhancing privacy by encrypting shared intermediate results. However, SFL currently lacks effective Byzantine robustness, a critical property that ensures model performance remains intact even when some participants act maliciously. Existing Byzantine-robust methods in 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":"2501.06953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-01-12T22:14:45Z","cross_cats_sorted":[],"title_canon_sha256":"1ded9e4d80487cd2127b80076facdc0c9ae20cffa472709e915edebd0c4be72c","abstract_canon_sha256":"9ea33dde2ec9af7148a943762c03e17ddecef3e1c7f43e6a3607eca4cc36ecf3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:12.950797Z","signature_b64":"uoZVqOWGJ2hQpbOAjaC7b62Ie7LCa1PqQTg9xbx0VtAykuhDokIyn7SfScRf11qxQVMeUXENUX6QqHxKBJFJBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"402ecd54f6ef0a61c5a8b8f373e00182ae5697451c6b79224a2c94311c02f342","last_reissued_at":"2026-07-05T10:00:12.950303Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:12.950303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ByzSFL: Achieving Byzantine-Robust Secure Federated Learning with Zero-Knowledge Proofs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chenghong Wang, Haixu Tang, Hyunghoon Cho, Lucila Ohno-Machado, Rui Zhu, Ye Dong, Yongming Fan, Zihao Wang","submitted_at":"2025-01-12T22:14:45Z","abstract_excerpt":"The advancement of AI models, especially those powered by deep learning, faces significant challenges in data-sensitive industries like healthcare and finance due to the distributed and private nature of data. Federated Learning (FL) and Secure Federated Learning (SFL) enable collaborative model training without data sharing, enhancing privacy by encrypting shared intermediate results. However, SFL currently lacks effective Byzantine robustness, a critical property that ensures model performance remains intact even when some participants act maliciously. Existing Byzantine-robust methods in FL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06953","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/2501.06953/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":"2501.06953","created_at":"2026-07-05T10:00:12.950376+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06953v1","created_at":"2026-07-05T10:00:12.950376+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06953","created_at":"2026-07-05T10:00:12.950376+00:00"},{"alias_kind":"pith_short_12","alias_value":"IAXM2VHW54FG","created_at":"2026-07-05T10:00:12.950376+00:00"},{"alias_kind":"pith_short_16","alias_value":"IAXM2VHW54FGDRNI","created_at":"2026-07-05T10:00:12.950376+00:00"},{"alias_kind":"pith_short_8","alias_value":"IAXM2VHW","created_at":"2026-07-05T10:00:12.950376+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06850","citing_title":"How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK","json":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK.json","graph_json":"https://pith.science/api/pith-number/IAXM2VHW54FGDRNIXDZXHYABQK/graph.json","events_json":"https://pith.science/api/pith-number/IAXM2VHW54FGDRNIXDZXHYABQK/events.json","paper":"https://pith.science/paper/IAXM2VHW"},"agent_actions":{"view_html":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK","download_json":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK.json","view_paper":"https://pith.science/paper/IAXM2VHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06953&json=true","fetch_graph":"https://pith.science/api/pith-number/IAXM2VHW54FGDRNIXDZXHYABQK/graph.json","fetch_events":"https://pith.science/api/pith-number/IAXM2VHW54FGDRNIXDZXHYABQK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK/action/storage_attestation","attest_author":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK/action/author_attestation","sign_citation":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK/action/citation_signature","submit_replication":"https://pith.science/pith/IAXM2VHW54FGDRNIXDZXHYABQK/action/replication_record"}},"created_at":"2026-07-05T10:00:12.950376+00:00","updated_at":"2026-07-05T10:00:12.950376+00:00"}