{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3O7I2K4S5UDTXL7KN2KWQJYQAW","short_pith_number":"pith:3O7I2K4S","schema_version":"1.0","canonical_sha256":"dbbe8d2b92ed073bafea6e9568271005802874832ba29761eded8a35e169a832","source":{"kind":"arxiv","id":"2309.10607","version":1},"attestation_state":"computed","paper":{"title":"SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chip-Hong Chang, Huawei Li, Jing Ye, Weiyang He, Xiaowei Li, Zizhen Liu","submitted_at":"2023-09-19T13:31:33Z","abstract_excerpt":"While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new security threats, of which poisoning attacks are particularly rampant and hard to defend without compromising privacy, performance or other desirable properties of FL. To tackle this problem, we propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model to supervise the training of aggregated model in each iteration. The purification is performed"},"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":"2309.10607","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2023-09-19T13:31:33Z","cross_cats_sorted":[],"title_canon_sha256":"e6c2bbc8c2bc88c046004072cc591bb746933c989044c81a6e1f84049b7552ec","abstract_canon_sha256":"46de6d7a8b94457b55418954f13e014aca3cc955d9f656d63f14a68a34cad67b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:52:14.638676Z","signature_b64":"3fp5UMKXq3Xcyz7jKmo2trTK8S/XjZP37utAJTLmLV3j672/9pyz4sDPyei1XpnShnXrfCSM7THAU52+x6npBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbbe8d2b92ed073bafea6e9568271005802874832ba29761eded8a35e169a832","last_reissued_at":"2026-07-05T06:52:14.638260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:52:14.638260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chip-Hong Chang, Huawei Li, Jing Ye, Weiyang He, Xiaowei Li, Zizhen Liu","submitted_at":"2023-09-19T13:31:33Z","abstract_excerpt":"While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new security threats, of which poisoning attacks are particularly rampant and hard to defend without compromising privacy, performance or other desirable properties of FL. To tackle this problem, we propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model to supervise the training of aggregated model in each iteration. The purification is performed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10607","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/2309.10607/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":"2309.10607","created_at":"2026-07-05T06:52:14.638320+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.10607v1","created_at":"2026-07-05T06:52:14.638320+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10607","created_at":"2026-07-05T06:52:14.638320+00:00"},{"alias_kind":"pith_short_12","alias_value":"3O7I2K4S5UDT","created_at":"2026-07-05T06:52:14.638320+00:00"},{"alias_kind":"pith_short_16","alias_value":"3O7I2K4S5UDTXL7K","created_at":"2026-07-05T06:52:14.638320+00:00"},{"alias_kind":"pith_short_8","alias_value":"3O7I2K4S","created_at":"2026-07-05T06:52:14.638320+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/3O7I2K4S5UDTXL7KN2KWQJYQAW","json":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW.json","graph_json":"https://pith.science/api/pith-number/3O7I2K4S5UDTXL7KN2KWQJYQAW/graph.json","events_json":"https://pith.science/api/pith-number/3O7I2K4S5UDTXL7KN2KWQJYQAW/events.json","paper":"https://pith.science/paper/3O7I2K4S"},"agent_actions":{"view_html":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW","download_json":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW.json","view_paper":"https://pith.science/paper/3O7I2K4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.10607&json=true","fetch_graph":"https://pith.science/api/pith-number/3O7I2K4S5UDTXL7KN2KWQJYQAW/graph.json","fetch_events":"https://pith.science/api/pith-number/3O7I2K4S5UDTXL7KN2KWQJYQAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW/action/storage_attestation","attest_author":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW/action/author_attestation","sign_citation":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW/action/citation_signature","submit_replication":"https://pith.science/pith/3O7I2K4S5UDTXL7KN2KWQJYQAW/action/replication_record"}},"created_at":"2026-07-05T06:52:14.638320+00:00","updated_at":"2026-07-05T06:52:14.638320+00:00"}