{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TXKWS662VGZ6MUAHPPYK3R32GS","short_pith_number":"pith:TXKWS662","schema_version":"1.0","canonical_sha256":"9dd5697bdaa9b3e650077bf0adc77a349797531a916a4e2f3fbaadf1d7c90bdc","source":{"kind":"arxiv","id":"2312.02074","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning is Better with Non-Homomorphic Encryption","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.CR","authors_text":"Abdulmajeed Alrowithi, Fahad Ali Albalawi, Konstantin Burlachenko, Peter Richtarik","submitted_at":"2023-12-04T17:37:41Z","abstract_excerpt":"Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. "},"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":"2312.02074","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-12-04T17:37:41Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"1bb6627d9b209be75bedfdad638a98c065e6695e78a67fed22bd7553b405c2bf","abstract_canon_sha256":"9d77a85c08f0f53004f36397c1d86cb64b64d7ad9e6e40102cd933c1a6f646ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:03.484573Z","signature_b64":"I52IMl/+SHdgzQvQpXRFo522MwngOak+qtNAUcW3Ygt6Kjv/mWuImFtWo6xVMnpoIkCTrlnnC7gkU65qGw3BCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dd5697bdaa9b3e650077bf0adc77a349797531a916a4e2f3fbaadf1d7c90bdc","last_reissued_at":"2026-07-05T07:20:03.484050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:03.484050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning is Better with Non-Homomorphic Encryption","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.CR","authors_text":"Abdulmajeed Alrowithi, Fahad Ali Albalawi, Konstantin Burlachenko, Peter Richtarik","submitted_at":"2023-12-04T17:37:41Z","abstract_excerpt":"Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02074","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/2312.02074/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":"2312.02074","created_at":"2026-07-05T07:20:03.484120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02074v1","created_at":"2026-07-05T07:20:03.484120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02074","created_at":"2026-07-05T07:20:03.484120+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXKWS662VGZ6","created_at":"2026-07-05T07:20:03.484120+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXKWS662VGZ6MUAH","created_at":"2026-07-05T07:20:03.484120+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXKWS662","created_at":"2026-07-05T07:20:03.484120+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/TXKWS662VGZ6MUAHPPYK3R32GS","json":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS.json","graph_json":"https://pith.science/api/pith-number/TXKWS662VGZ6MUAHPPYK3R32GS/graph.json","events_json":"https://pith.science/api/pith-number/TXKWS662VGZ6MUAHPPYK3R32GS/events.json","paper":"https://pith.science/paper/TXKWS662"},"agent_actions":{"view_html":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS","download_json":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS.json","view_paper":"https://pith.science/paper/TXKWS662","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02074&json=true","fetch_graph":"https://pith.science/api/pith-number/TXKWS662VGZ6MUAHPPYK3R32GS/graph.json","fetch_events":"https://pith.science/api/pith-number/TXKWS662VGZ6MUAHPPYK3R32GS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS/action/storage_attestation","attest_author":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS/action/author_attestation","sign_citation":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS/action/citation_signature","submit_replication":"https://pith.science/pith/TXKWS662VGZ6MUAHPPYK3R32GS/action/replication_record"}},"created_at":"2026-07-05T07:20:03.484120+00:00","updated_at":"2026-07-05T07:20:03.484120+00:00"}