{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Z2AXPVY6TYTTBMAJ5AQB5C72ZM","short_pith_number":"pith:Z2AXPVY6","schema_version":"1.0","canonical_sha256":"ce8177d71e9e2730b009e8201e8bfacb3ec0f20a95438911a432fa59d3dc8ff4","source":{"kind":"arxiv","id":"2608.03267","version":1},"attestation_state":"computed","paper":{"title":"FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ke Cheng, Lele Zheng, Ruijie Hu, Tao Zhang, Yulong Shen","submitted_at":"2026-08-04T07:39:40Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) enables communication-efficient federated fine-tuning of pretrained language models. However, integrating differential privacy (DP) into federated LoRA remains challenging: independently perturbing and aggregating its two low-rank matrices can cause aggregation mismatch and the quadratic noise term. Existing methods mitigate these issues by freezing one low-rank matrix but still rely on Euclidean aggregation, which is basis-dependent and may distort the global update. To address this limitation, we propose FedGSA, a geometry-consistent aggregation framework for diffe"},"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":"2608.03267","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2026-08-04T07:39:40Z","cross_cats_sorted":[],"title_canon_sha256":"8389eda3b76cdf0cf3093302691818f84949aa956bd1270d803fabe3d765a1ab","abstract_canon_sha256":"a2fd0c1bcf4828e128d320b4408866e655191591c269bc87ce6e8df63665fbd5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T00:46:35.936769Z","signature_b64":"pfs4FP/3eM1qOfzoLcMYVimAyJfTaPZ3m9FQqmp0mj+kKBDlkPTdv+AjUlDnzOmpfqeTpiVn1z2CrLKqdJfEAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce8177d71e9e2730b009e8201e8bfacb3ec0f20a95438911a432fa59d3dc8ff4","last_reissued_at":"2026-08-05T00:46:35.934424Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T00:46:35.934424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ke Cheng, Lele Zheng, Ruijie Hu, Tao Zhang, Yulong Shen","submitted_at":"2026-08-04T07:39:40Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) enables communication-efficient federated fine-tuning of pretrained language models. However, integrating differential privacy (DP) into federated LoRA remains challenging: independently perturbing and aggregating its two low-rank matrices can cause aggregation mismatch and the quadratic noise term. Existing methods mitigate these issues by freezing one low-rank matrix but still rely on Euclidean aggregation, which is basis-dependent and may distort the global update. To address this limitation, we propose FedGSA, a geometry-consistent aggregation framework for diffe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03267","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/2608.03267/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":"2608.03267","created_at":"2026-08-05T00:46:35.935246+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.03267v1","created_at":"2026-08-05T00:46:35.935246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03267","created_at":"2026-08-05T00:46:35.935246+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2AXPVY6TYTT","created_at":"2026-08-05T00:46:35.935246+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2AXPVY6TYTTBMAJ","created_at":"2026-08-05T00:46:35.935246+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2AXPVY6","created_at":"2026-08-05T00:46:35.935246+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/Z2AXPVY6TYTTBMAJ5AQB5C72ZM","json":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM.json","graph_json":"https://pith.science/api/pith-number/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/graph.json","events_json":"https://pith.science/api/pith-number/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/events.json","paper":"https://pith.science/paper/Z2AXPVY6"},"agent_actions":{"view_html":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM","download_json":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM.json","view_paper":"https://pith.science/paper/Z2AXPVY6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.03267&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/action/storage_attestation","attest_author":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/action/author_attestation","sign_citation":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/action/citation_signature","submit_replication":"https://pith.science/pith/Z2AXPVY6TYTTBMAJ5AQB5C72ZM/action/replication_record"}},"created_at":"2026-08-05T00:46:35.935246+00:00","updated_at":"2026-08-05T00:46:35.935246+00:00"}