{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X22QLIIA3MM3SH4HMN7PXSEYLV","short_pith_number":"pith:X22QLIIA","schema_version":"1.0","canonical_sha256":"beb505a100db19b91f87637efbc8985d49265a0c8d3905fc2eaadd1070596e1d","source":{"kind":"arxiv","id":"2403.06067","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning: Attacks, Defenses, Opportunities, and Challenges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Behzad Beigzadeh, Ghazaleh Shirvani, Saeid Ghasemshirazi","submitted_at":"2024-03-10T03:05:59Z","abstract_excerpt":"Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the community's trust since its security and privacy implications are controversial. FL's security and privacy concerns must be discovered, analyzed, and recorded before widespread usage and adoption. A solid comprehension of risk variables allows an FL practitioner to construct a secure environment and provide researchers with a clear perspective of potential study fields, making 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":"2403.06067","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-03-10T03:05:59Z","cross_cats_sorted":[],"title_canon_sha256":"6c197707659f1874f645e07d952be388d00b9f4fec7b51c2647545fdc82df908","abstract_canon_sha256":"d67c7df6abd80b0768b44311b5711edad1fe1d03f7b1acd670a7c87acd254f64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:17.683901Z","signature_b64":"CQEhmP5StaXKL7SdeM5WTeWa6LdFFkZfAsnImfdAkIcvtq1BS0zcoGMSx5R2pTdQiioi2zUsay++CLfC5Bb6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"beb505a100db19b91f87637efbc8985d49265a0c8d3905fc2eaadd1070596e1d","last_reissued_at":"2026-07-05T07:54:17.683416Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:17.683416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning: Attacks, Defenses, Opportunities, and Challenges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Behzad Beigzadeh, Ghazaleh Shirvani, Saeid Ghasemshirazi","submitted_at":"2024-03-10T03:05:59Z","abstract_excerpt":"Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the community's trust since its security and privacy implications are controversial. FL's security and privacy concerns must be discovered, analyzed, and recorded before widespread usage and adoption. A solid comprehension of risk variables allows an FL practitioner to construct a secure environment and provide researchers with a clear perspective of potential study fields, making FL "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06067","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/2403.06067/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":"2403.06067","created_at":"2026-07-05T07:54:17.683473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06067v1","created_at":"2026-07-05T07:54:17.683473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06067","created_at":"2026-07-05T07:54:17.683473+00:00"},{"alias_kind":"pith_short_12","alias_value":"X22QLIIA3MM3","created_at":"2026-07-05T07:54:17.683473+00:00"},{"alias_kind":"pith_short_16","alias_value":"X22QLIIA3MM3SH4H","created_at":"2026-07-05T07:54:17.683473+00:00"},{"alias_kind":"pith_short_8","alias_value":"X22QLIIA","created_at":"2026-07-05T07:54:17.683473+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/X22QLIIA3MM3SH4HMN7PXSEYLV","json":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV.json","graph_json":"https://pith.science/api/pith-number/X22QLIIA3MM3SH4HMN7PXSEYLV/graph.json","events_json":"https://pith.science/api/pith-number/X22QLIIA3MM3SH4HMN7PXSEYLV/events.json","paper":"https://pith.science/paper/X22QLIIA"},"agent_actions":{"view_html":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV","download_json":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV.json","view_paper":"https://pith.science/paper/X22QLIIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06067&json=true","fetch_graph":"https://pith.science/api/pith-number/X22QLIIA3MM3SH4HMN7PXSEYLV/graph.json","fetch_events":"https://pith.science/api/pith-number/X22QLIIA3MM3SH4HMN7PXSEYLV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV/action/storage_attestation","attest_author":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV/action/author_attestation","sign_citation":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV/action/citation_signature","submit_replication":"https://pith.science/pith/X22QLIIA3MM3SH4HMN7PXSEYLV/action/replication_record"}},"created_at":"2026-07-05T07:54:17.683473+00:00","updated_at":"2026-07-05T07:54:17.683473+00:00"}