{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AA5MRDZHBBWONCJEDZKY3SZX4W","short_pith_number":"pith:AA5MRDZH","schema_version":"1.0","canonical_sha256":"003ac88f27086ce689241e558dcb37e5ba4b9a7d396e12492295d42d454068d9","source":{"kind":"arxiv","id":"2302.09832","version":4},"attestation_state":"computed","paper":{"title":"TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Grigory Malinovsky, Ivan Agarsk\\'y, Laurent Condat, Peter Richt\\'arik","submitted_at":"2023-02-20T08:37:44Z","abstract_excerpt":"In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate this burden, two strategies have emerged: 1) local training (LT), which reduces communication frequency by performing multiple local computations between rounds, and 2) compression (CC), which consists of transmitting lower-dimensional, compact representations. Recent theoretical advances have successfully combined LT and CC to achieve doubly-accelerated communication rates, with respect to both condition number and m"},"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":"2302.09832","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-20T08:37:44Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"f5880db899b45e8aa5a2ef03ee63a448f0f4f13cc7f71bab5a52bd8e0429ef59","abstract_canon_sha256":"3edbe586cd8506908cc52ec9f571dc82a7bf947d66bab2b93c6d0c8368161fe5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:02.541266Z","signature_b64":"TImfp19BJql6Von+MzFa+1JDqQljIN+5kR0l+YOrkhRE4fusqQUkxP7G2ZJhhNDdUxF/WOnpbc7KkI1+Yo1DBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"003ac88f27086ce689241e558dcb37e5ba4b9a7d396e12492295d42d454068d9","last_reissued_at":"2026-06-09T01:05:02.540784Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:02.540784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Grigory Malinovsky, Ivan Agarsk\\'y, Laurent Condat, Peter Richt\\'arik","submitted_at":"2023-02-20T08:37:44Z","abstract_excerpt":"In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate this burden, two strategies have emerged: 1) local training (LT), which reduces communication frequency by performing multiple local computations between rounds, and 2) compression (CC), which consists of transmitting lower-dimensional, compact representations. Recent theoretical advances have successfully combined LT and CC to achieve doubly-accelerated communication rates, with respect to both condition number and m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09832","kind":"arxiv","version":4},"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/2302.09832/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":"2302.09832","created_at":"2026-06-09T01:05:02.540837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.09832v4","created_at":"2026-06-09T01:05:02.540837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09832","created_at":"2026-06-09T01:05:02.540837+00:00"},{"alias_kind":"pith_short_12","alias_value":"AA5MRDZHBBWO","created_at":"2026-06-09T01:05:02.540837+00:00"},{"alias_kind":"pith_short_16","alias_value":"AA5MRDZHBBWONCJE","created_at":"2026-06-09T01:05:02.540837+00:00"},{"alias_kind":"pith_short_8","alias_value":"AA5MRDZH","created_at":"2026-06-09T01:05:02.540837+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2310.07983","citing_title":"Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13434","citing_title":"Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W","json":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W.json","graph_json":"https://pith.science/api/pith-number/AA5MRDZHBBWONCJEDZKY3SZX4W/graph.json","events_json":"https://pith.science/api/pith-number/AA5MRDZHBBWONCJEDZKY3SZX4W/events.json","paper":"https://pith.science/paper/AA5MRDZH"},"agent_actions":{"view_html":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W","download_json":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W.json","view_paper":"https://pith.science/paper/AA5MRDZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.09832&json=true","fetch_graph":"https://pith.science/api/pith-number/AA5MRDZHBBWONCJEDZKY3SZX4W/graph.json","fetch_events":"https://pith.science/api/pith-number/AA5MRDZHBBWONCJEDZKY3SZX4W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W/action/storage_attestation","attest_author":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W/action/author_attestation","sign_citation":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W/action/citation_signature","submit_replication":"https://pith.science/pith/AA5MRDZHBBWONCJEDZKY3SZX4W/action/replication_record"}},"created_at":"2026-06-09T01:05:02.540837+00:00","updated_at":"2026-06-09T01:05:02.540837+00:00"}