{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VUUNLLLGQCNPZOE4MIDMSJDRBC","short_pith_number":"pith:VUUNLLLG","schema_version":"1.0","canonical_sha256":"ad28d5ad66809afcb89c6206c9247108943749b9dee40d18036eef8d2a7a1015","source":{"kind":"arxiv","id":"2401.10375","version":3},"attestation_state":"computed","paper":{"title":"Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","cs.LG"],"primary_cat":"cs.CR","authors_text":"Chen Wu, Jiaqi Wang, Xi Li","submitted_at":"2024-01-18T20:56:42Z","abstract_excerpt":"Federated Learning (FL), a privacy-preserving machine learning framework, faces significant data-related challenges. For example, the lack of suitable public datasets leads to ineffective information exchange, especially in heterogeneous environments with uneven data distribution. Foundation Models (FMs) offer a promising solution by generating synthetic datasets that mimic client data distributions, aiding model initialization and knowledge sharing among clients. However, the interaction between FMs and FL introduces new attack vectors that remain largely unexplored. This work therefore asses"},"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":"2401.10375","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-01-18T20:56:42Z","cross_cats_sorted":["cs.DC","cs.LG"],"title_canon_sha256":"207b15f62e22a7b1f49eb1202f545ca77f2737ac73eb009fbb8bf0a91ea22d8b","abstract_canon_sha256":"37a3bd9d0a2c543177cd21289cc15e7237bae9d2f82ded650a0b7ca0a86be67c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:03.434983Z","signature_b64":"vzFEgSb046I+lZr8ZdlpL0GhtoBqAr3PmTWvwx7N57eLt5t4gx1Ta2g8xIKRHr8p6wo/mRmRV38tjzuRhAzfAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad28d5ad66809afcb89c6206c9247108943749b9dee40d18036eef8d2a7a1015","last_reissued_at":"2026-07-05T10:51:03.434497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:03.434497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","cs.LG"],"primary_cat":"cs.CR","authors_text":"Chen Wu, Jiaqi Wang, Xi Li","submitted_at":"2024-01-18T20:56:42Z","abstract_excerpt":"Federated Learning (FL), a privacy-preserving machine learning framework, faces significant data-related challenges. For example, the lack of suitable public datasets leads to ineffective information exchange, especially in heterogeneous environments with uneven data distribution. Foundation Models (FMs) offer a promising solution by generating synthetic datasets that mimic client data distributions, aiding model initialization and knowledge sharing among clients. However, the interaction between FMs and FL introduces new attack vectors that remain largely unexplored. This work therefore asses"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10375","kind":"arxiv","version":3},"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/2401.10375/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":"2401.10375","created_at":"2026-07-05T10:51:03.434554+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.10375v3","created_at":"2026-07-05T10:51:03.434554+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10375","created_at":"2026-07-05T10:51:03.434554+00:00"},{"alias_kind":"pith_short_12","alias_value":"VUUNLLLGQCNP","created_at":"2026-07-05T10:51:03.434554+00:00"},{"alias_kind":"pith_short_16","alias_value":"VUUNLLLGQCNPZOE4","created_at":"2026-07-05T10:51:03.434554+00:00"},{"alias_kind":"pith_short_8","alias_value":"VUUNLLLG","created_at":"2026-07-05T10:51:03.434554+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17110","citing_title":"Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2501.18416","citing_title":"Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19940","citing_title":"Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC","json":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC.json","graph_json":"https://pith.science/api/pith-number/VUUNLLLGQCNPZOE4MIDMSJDRBC/graph.json","events_json":"https://pith.science/api/pith-number/VUUNLLLGQCNPZOE4MIDMSJDRBC/events.json","paper":"https://pith.science/paper/VUUNLLLG"},"agent_actions":{"view_html":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC","download_json":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC.json","view_paper":"https://pith.science/paper/VUUNLLLG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.10375&json=true","fetch_graph":"https://pith.science/api/pith-number/VUUNLLLGQCNPZOE4MIDMSJDRBC/graph.json","fetch_events":"https://pith.science/api/pith-number/VUUNLLLGQCNPZOE4MIDMSJDRBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC/action/storage_attestation","attest_author":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC/action/author_attestation","sign_citation":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC/action/citation_signature","submit_replication":"https://pith.science/pith/VUUNLLLGQCNPZOE4MIDMSJDRBC/action/replication_record"}},"created_at":"2026-07-05T10:51:03.434554+00:00","updated_at":"2026-07-05T10:51:03.434554+00:00"}