{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:EZHXEAS2RJ5KAHK3QFCG725UVJ","short_pith_number":"pith:EZHXEAS2","schema_version":"1.0","canonical_sha256":"264f72025a8a7aa01d5b81446febb4aa6537a5a448bf5c0f62c25e574ec16b70","source":{"kind":"arxiv","id":"2003.10615","version":1},"attestation_state":"computed","paper":{"title":"Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"math.OC","authors_text":"Hao Chen, H. Vincent Poor, Mikael Skoglund, Ming Xiao, Yu Ye","submitted_at":"2020-03-24T02:02:25Z","abstract_excerpt":"The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk alg"},"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":"2003.10615","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2020-03-24T02:02:25Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"3fd5b9a97aed6207a851c3c2d4d11149d33ff99bc38b725c78f025bc687a4d5c","abstract_canon_sha256":"811c9a613d51713a841ba9d120a40bd65d67d85bf8bf2672ee2994e1fa85eb8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:48.931274Z","signature_b64":"MOkrzyBQmE12WyMe7dpLL1McvfLUdIpVfQOXolDBG9Tas0NFZND71U1nBH8DwxnSMEKdCZd+1wmDwB+CG+/tDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"264f72025a8a7aa01d5b81446febb4aa6537a5a448bf5c0f62c25e574ec16b70","last_reissued_at":"2026-07-05T01:55:48.930808Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:48.930808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"math.OC","authors_text":"Hao Chen, H. Vincent Poor, Mikael Skoglund, Ming Xiao, Yu Ye","submitted_at":"2020-03-24T02:02:25Z","abstract_excerpt":"The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk alg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.10615","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/2003.10615/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":"2003.10615","created_at":"2026-07-05T01:55:48.930864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.10615v1","created_at":"2026-07-05T01:55:48.930864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.10615","created_at":"2026-07-05T01:55:48.930864+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZHXEAS2RJ5K","created_at":"2026-07-05T01:55:48.930864+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZHXEAS2RJ5KAHK3","created_at":"2026-07-05T01:55:48.930864+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZHXEAS2","created_at":"2026-07-05T01:55:48.930864+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/EZHXEAS2RJ5KAHK3QFCG725UVJ","json":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ.json","graph_json":"https://pith.science/api/pith-number/EZHXEAS2RJ5KAHK3QFCG725UVJ/graph.json","events_json":"https://pith.science/api/pith-number/EZHXEAS2RJ5KAHK3QFCG725UVJ/events.json","paper":"https://pith.science/paper/EZHXEAS2"},"agent_actions":{"view_html":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ","download_json":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ.json","view_paper":"https://pith.science/paper/EZHXEAS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.10615&json=true","fetch_graph":"https://pith.science/api/pith-number/EZHXEAS2RJ5KAHK3QFCG725UVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EZHXEAS2RJ5KAHK3QFCG725UVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ/action/storage_attestation","attest_author":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ/action/author_attestation","sign_citation":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ/action/citation_signature","submit_replication":"https://pith.science/pith/EZHXEAS2RJ5KAHK3QFCG725UVJ/action/replication_record"}},"created_at":"2026-07-05T01:55:48.930864+00:00","updated_at":"2026-07-05T01:55:48.930864+00:00"}