{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TD7H6HBE6EOQZ3EKAASVLNJURP","short_pith_number":"pith:TD7H6HBE","schema_version":"1.0","canonical_sha256":"98fe7f1c24f11d0cec8a002555b5348bc9b00fe96458eab73e09ffd30412c245","source":{"kind":"arxiv","id":"2302.10755","version":1},"attestation_state":"computed","paper":{"title":"Federated Gradient Matching Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.NA"],"primary_cat":"cs.LG","authors_text":"Deanna Needell, Halyun Jeong, Jing Qin","submitted_at":"2023-02-20T16:26:29Z","abstract_excerpt":"Traditional machine learning techniques require centralizing all training data on one server or data hub. Due to the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structu"},"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":"2302.10755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-20T16:26:29Z","cross_cats_sorted":["cs.IT","cs.NA","math.IT","math.NA"],"title_canon_sha256":"337b0cb39581f329065ae9dd9d76da17e332c36349161414c4c7831cca3ccb62","abstract_canon_sha256":"5d6f84cc6b740c77652451455ee81f14c49ce5d2c7b0c573953e4e2395afd9f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:21.366649Z","signature_b64":"wGjvazJO8yU6h93RbuKKX3EdXGXkVhazmLOuU874tPRBoJKNl/bRsWDhMAPB3xG7JYuSBENyMRuPWHnWAkMnBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98fe7f1c24f11d0cec8a002555b5348bc9b00fe96458eab73e09ffd30412c245","last_reissued_at":"2026-07-05T05:44:21.366234Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:21.366234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Gradient Matching Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.NA"],"primary_cat":"cs.LG","authors_text":"Deanna Needell, Halyun Jeong, Jing Qin","submitted_at":"2023-02-20T16:26:29Z","abstract_excerpt":"Traditional machine learning techniques require centralizing all training data on one server or data hub. Due to the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.10755","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/2302.10755/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":"2302.10755","created_at":"2026-07-05T05:44:21.366290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.10755v1","created_at":"2026-07-05T05:44:21.366290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.10755","created_at":"2026-07-05T05:44:21.366290+00:00"},{"alias_kind":"pith_short_12","alias_value":"TD7H6HBE6EOQ","created_at":"2026-07-05T05:44:21.366290+00:00"},{"alias_kind":"pith_short_16","alias_value":"TD7H6HBE6EOQZ3EK","created_at":"2026-07-05T05:44:21.366290+00:00"},{"alias_kind":"pith_short_8","alias_value":"TD7H6HBE","created_at":"2026-07-05T05:44:21.366290+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/TD7H6HBE6EOQZ3EKAASVLNJURP","json":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP.json","graph_json":"https://pith.science/api/pith-number/TD7H6HBE6EOQZ3EKAASVLNJURP/graph.json","events_json":"https://pith.science/api/pith-number/TD7H6HBE6EOQZ3EKAASVLNJURP/events.json","paper":"https://pith.science/paper/TD7H6HBE"},"agent_actions":{"view_html":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP","download_json":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP.json","view_paper":"https://pith.science/paper/TD7H6HBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.10755&json=true","fetch_graph":"https://pith.science/api/pith-number/TD7H6HBE6EOQZ3EKAASVLNJURP/graph.json","fetch_events":"https://pith.science/api/pith-number/TD7H6HBE6EOQZ3EKAASVLNJURP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP/action/storage_attestation","attest_author":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP/action/author_attestation","sign_citation":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP/action/citation_signature","submit_replication":"https://pith.science/pith/TD7H6HBE6EOQZ3EKAASVLNJURP/action/replication_record"}},"created_at":"2026-07-05T05:44:21.366290+00:00","updated_at":"2026-07-05T05:44:21.366290+00:00"}