{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VRHIZM5MK2Q25DDIJ7OA4NSTXU","short_pith_number":"pith:VRHIZM5M","schema_version":"1.0","canonical_sha256":"ac4e8cb3ac56a1ae8c684fdc0e3653bd13be70122841200d279fb9f384c675ed","source":{"kind":"arxiv","id":"2305.16297","version":3},"attestation_state":"computed","paper":{"title":"Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Kun Yuan, Xinmeng Huang, Yutong He","submitted_at":"2023-05-25T17:51:23Z","abstract_excerpt":"Communication compression is a common technique in distributed optimization that can alleviate communication overhead by transmitting compressed gradients and model parameters. However, compression can introduce information distortion, which slows down convergence and incurs more communication rounds to achieve desired solutions. Given the trade-off between lower per-round communication costs and additional rounds of communication, it is unclear whether communication compression reduces the total communication cost.\n  This paper explores the conditions under which unbiased compression, a widel"},"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":"2305.16297","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-25T17:51:23Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"e28eb050de24dbf58a1228a655979aacd8006008721f70c1a0be658f287a1940","abstract_canon_sha256":"8332b9ef795de7efb7804c1b82be7013914ad0f43905ff8b2453f72553de6006"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:32:18.605294Z","signature_b64":"BPvHmEiuVjUTBB8gb2cQj1Z38bleWm8wKZUKwnS4PPzKJF30FHqWzDu6BYgF/yBXrhWBOJMgmlP6q+8XaYdUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac4e8cb3ac56a1ae8c684fdc0e3653bd13be70122841200d279fb9f384c675ed","last_reissued_at":"2026-07-05T07:32:18.604775Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:32:18.604775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Kun Yuan, Xinmeng Huang, Yutong He","submitted_at":"2023-05-25T17:51:23Z","abstract_excerpt":"Communication compression is a common technique in distributed optimization that can alleviate communication overhead by transmitting compressed gradients and model parameters. However, compression can introduce information distortion, which slows down convergence and incurs more communication rounds to achieve desired solutions. Given the trade-off between lower per-round communication costs and additional rounds of communication, it is unclear whether communication compression reduces the total communication cost.\n  This paper explores the conditions under which unbiased compression, a widel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16297","kind":"arxiv","version":3},"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/2305.16297/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":"2305.16297","created_at":"2026-07-05T07:32:18.604838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16297v3","created_at":"2026-07-05T07:32:18.604838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16297","created_at":"2026-07-05T07:32:18.604838+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRHIZM5MK2Q2","created_at":"2026-07-05T07:32:18.604838+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRHIZM5MK2Q25DDI","created_at":"2026-07-05T07:32:18.604838+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRHIZM5M","created_at":"2026-07-05T07:32:18.604838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.12409","citing_title":"The Stochastic Multi-Proximal Method for Nonsmooth Optimization","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU","json":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU.json","graph_json":"https://pith.science/api/pith-number/VRHIZM5MK2Q25DDIJ7OA4NSTXU/graph.json","events_json":"https://pith.science/api/pith-number/VRHIZM5MK2Q25DDIJ7OA4NSTXU/events.json","paper":"https://pith.science/paper/VRHIZM5M"},"agent_actions":{"view_html":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU","download_json":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU.json","view_paper":"https://pith.science/paper/VRHIZM5M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16297&json=true","fetch_graph":"https://pith.science/api/pith-number/VRHIZM5MK2Q25DDIJ7OA4NSTXU/graph.json","fetch_events":"https://pith.science/api/pith-number/VRHIZM5MK2Q25DDIJ7OA4NSTXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU/action/storage_attestation","attest_author":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU/action/author_attestation","sign_citation":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU/action/citation_signature","submit_replication":"https://pith.science/pith/VRHIZM5MK2Q25DDIJ7OA4NSTXU/action/replication_record"}},"created_at":"2026-07-05T07:32:18.604838+00:00","updated_at":"2026-07-05T07:32:18.604838+00:00"}