{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4KVBUQZFAKXCTAJEH6SNIJXAFO","short_pith_number":"pith:4KVBUQZF","schema_version":"1.0","canonical_sha256":"e2aa1a432502ae2981243fa4d426e02bac76f8dddf9bae6f0e838ce59a4727b7","source":{"kind":"arxiv","id":"2211.15893","version":1},"attestation_state":"computed","paper":{"title":"Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Jie Fu, Xiao Han, Zhili Chen","submitted_at":"2022-11-29T03:20:40Z","abstract_excerpt":"Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning to protect data privacy, but the noise added may degrade the learning performance much. Typically, in previous work, training parameters were clipped equally and noises were added uniformly. The heterogeneity and convergence of training par"},"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":"2211.15893","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-29T03:20:40Z","cross_cats_sorted":["cs.CR","cs.DC"],"title_canon_sha256":"07e8c76b9dc48f70fe11e36b8a25133253e87b35ca3f73296a0c5e802a1742c9","abstract_canon_sha256":"485d7d8071a931787808f768bb55ee191a962c8116a3299b352e9bec7fc12144"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:19.515604Z","signature_b64":"qmf/bgTsYB/gzDev65MsNgy/2W8oqOIKcNn5cEf1ShtlaW54v+mEcDrBQCqqQHiZVWoI9V/vcSZJuGeQBcBEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2aa1a432502ae2981243fa4d426e02bac76f8dddf9bae6f0e838ce59a4727b7","last_reissued_at":"2026-07-05T05:20:19.515182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:19.515182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Jie Fu, Xiao Han, Zhili Chen","submitted_at":"2022-11-29T03:20:40Z","abstract_excerpt":"Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning to protect data privacy, but the noise added may degrade the learning performance much. Typically, in previous work, training parameters were clipped equally and noises were added uniformly. The heterogeneity and convergence of training par"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.15893","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/2211.15893/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":"2211.15893","created_at":"2026-07-05T05:20:19.515238+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.15893v1","created_at":"2026-07-05T05:20:19.515238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.15893","created_at":"2026-07-05T05:20:19.515238+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KVBUQZFAKXC","created_at":"2026-07-05T05:20:19.515238+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KVBUQZFAKXCTAJE","created_at":"2026-07-05T05:20:19.515238+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KVBUQZF","created_at":"2026-07-05T05:20:19.515238+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/4KVBUQZFAKXCTAJEH6SNIJXAFO","json":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO.json","graph_json":"https://pith.science/api/pith-number/4KVBUQZFAKXCTAJEH6SNIJXAFO/graph.json","events_json":"https://pith.science/api/pith-number/4KVBUQZFAKXCTAJEH6SNIJXAFO/events.json","paper":"https://pith.science/paper/4KVBUQZF"},"agent_actions":{"view_html":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO","download_json":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO.json","view_paper":"https://pith.science/paper/4KVBUQZF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.15893&json=true","fetch_graph":"https://pith.science/api/pith-number/4KVBUQZFAKXCTAJEH6SNIJXAFO/graph.json","fetch_events":"https://pith.science/api/pith-number/4KVBUQZFAKXCTAJEH6SNIJXAFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO/action/storage_attestation","attest_author":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO/action/author_attestation","sign_citation":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO/action/citation_signature","submit_replication":"https://pith.science/pith/4KVBUQZFAKXCTAJEH6SNIJXAFO/action/replication_record"}},"created_at":"2026-07-05T05:20:19.515238+00:00","updated_at":"2026-07-05T05:20:19.515238+00:00"}