{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2Z67CUYRFXCZR7OXV7EQQ6SKHF","short_pith_number":"pith:2Z67CUYR","schema_version":"1.0","canonical_sha256":"d67df153112dc598fdd7afc9087a4a39696694eea160d1bcd71cd12c298ec2a7","source":{"kind":"arxiv","id":"2507.07316","version":1},"attestation_state":"computed","paper":{"title":"AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Md Abrar Jahin, Md. Jakir Hossen, M. F. Mridha, Nafiz Fahad, Taufikur Rahman Fuad","submitted_at":"2025-07-09T22:29:02Z","abstract_excerpt":"Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced expressivity. We introduce AdeptHEQ-FL, a unified hybrid classical-quantum FL framework that integrates (i) a hybrid CNN-PQC architecture for expressive decentralized learning, (ii) an adaptive accuracy-weighted aggregation scheme leveraging differentially private validation accuracies, (iii) selective"},"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":"2507.07316","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T22:29:02Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"eede2d2519e4e314ba4531d36b5c0a690cdd3ee867319d2f88f71b7fdf404bf3","abstract_canon_sha256":"ff936d6a05f1d9fb751dba84ab307a8403c2ed5e659e6ec8be589073f343813f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:33.510226Z","signature_b64":"xd7uEr9D9l652cdDtXj1bcG9IlylxQe59FZL2yW55TpyfFHLFkwjKEwX9oSa2lX5IMRzC5JlxgeSXSzxf/iUAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d67df153112dc598fdd7afc9087a4a39696694eea160d1bcd71cd12c298ec2a7","last_reissued_at":"2026-05-18T02:44:33.509720Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:33.509720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Md Abrar Jahin, Md. Jakir Hossen, M. F. Mridha, Nafiz Fahad, Taufikur Rahman Fuad","submitted_at":"2025-07-09T22:29:02Z","abstract_excerpt":"Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced expressivity. We introduce AdeptHEQ-FL, a unified hybrid classical-quantum FL framework that integrates (i) a hybrid CNN-PQC architecture for expressive decentralized learning, (ii) an adaptive accuracy-weighted aggregation scheme leveraging differentially private validation accuracies, (iii) selective"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07316","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":""},"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":"2507.07316","created_at":"2026-05-18T02:44:33.509795+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.07316v1","created_at":"2026-05-18T02:44:33.509795+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07316","created_at":"2026-05-18T02:44:33.509795+00:00"},{"alias_kind":"pith_short_12","alias_value":"2Z67CUYRFXCZ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"2Z67CUYRFXCZR7OX","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"2Z67CUYR","created_at":"2026-05-18T12:33:37.589309+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/2Z67CUYRFXCZR7OXV7EQQ6SKHF","json":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF.json","graph_json":"https://pith.science/api/pith-number/2Z67CUYRFXCZR7OXV7EQQ6SKHF/graph.json","events_json":"https://pith.science/api/pith-number/2Z67CUYRFXCZR7OXV7EQQ6SKHF/events.json","paper":"https://pith.science/paper/2Z67CUYR"},"agent_actions":{"view_html":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF","download_json":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF.json","view_paper":"https://pith.science/paper/2Z67CUYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.07316&json=true","fetch_graph":"https://pith.science/api/pith-number/2Z67CUYRFXCZR7OXV7EQQ6SKHF/graph.json","fetch_events":"https://pith.science/api/pith-number/2Z67CUYRFXCZR7OXV7EQQ6SKHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF/action/storage_attestation","attest_author":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF/action/author_attestation","sign_citation":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF/action/citation_signature","submit_replication":"https://pith.science/pith/2Z67CUYRFXCZR7OXV7EQQ6SKHF/action/replication_record"}},"created_at":"2026-05-18T02:44:33.509795+00:00","updated_at":"2026-05-18T02:44:33.509795+00:00"}