{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DMZKJ5H2ZOQRNT3ZKYMHZHJWIL","short_pith_number":"pith:DMZKJ5H2","schema_version":"1.0","canonical_sha256":"1b32a4f4facba116cf7956187c9d3642e113c5de36991f01b9bed50598cd307e","source":{"kind":"arxiv","id":"1611.07555","version":1},"attestation_state":"computed","paper":{"title":"Randomized Distributed Mean Estimation: Accuracy vs Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.NA","stat.ML"],"primary_cat":"cs.DC","authors_text":"Jakub Kone\\v{c}n\\'y, Peter Richt\\'arik","submitted_at":"2016-11-22T22:18:36Z","abstract_excerpt":"We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subproblem in many applications, including reduce-all operations within algorithms for distributed and federated optimization and learning. We propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. O"},"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":"1611.07555","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-11-22T22:18:36Z","cross_cats_sorted":["math.NA","stat.ML"],"title_canon_sha256":"12f56c8ad921e3adce81522179a5d9292259e72b664a2b26c3498f1988c31de8","abstract_canon_sha256":"1750fc2f36db851c64f14e264f761820697ed07e223fd2ba6e204241e73bae85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:00.505440Z","signature_b64":"nRGIWyzZnevN1I8WMUMpGJ0uiP9HlmSAdhNZSJWb0s4DUli1YohFiv4L+PcOI37gHZkGAngf6DeQQlq6VJ4sAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b32a4f4facba116cf7956187c9d3642e113c5de36991f01b9bed50598cd307e","last_reissued_at":"2026-05-18T00:57:00.504694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:00.504694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Randomized Distributed Mean Estimation: Accuracy vs Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.NA","stat.ML"],"primary_cat":"cs.DC","authors_text":"Jakub Kone\\v{c}n\\'y, Peter Richt\\'arik","submitted_at":"2016-11-22T22:18:36Z","abstract_excerpt":"We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subproblem in many applications, including reduce-all operations within algorithms for distributed and federated optimization and learning. We propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07555","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":""},"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":"1611.07555","created_at":"2026-05-18T00:57:00.504827+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.07555v1","created_at":"2026-05-18T00:57:00.504827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07555","created_at":"2026-05-18T00:57:00.504827+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMZKJ5H2ZOQR","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMZKJ5H2ZOQRNT3Z","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMZKJ5H2","created_at":"2026-05-18T12:30:12.583610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1610.05492","citing_title":"Federated Learning: Strategies for Improving Communication Efficiency","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL","json":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL.json","graph_json":"https://pith.science/api/pith-number/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/graph.json","events_json":"https://pith.science/api/pith-number/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/events.json","paper":"https://pith.science/paper/DMZKJ5H2"},"agent_actions":{"view_html":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL","download_json":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL.json","view_paper":"https://pith.science/paper/DMZKJ5H2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.07555&json=true","fetch_graph":"https://pith.science/api/pith-number/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/graph.json","fetch_events":"https://pith.science/api/pith-number/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/action/storage_attestation","attest_author":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/action/author_attestation","sign_citation":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/action/citation_signature","submit_replication":"https://pith.science/pith/DMZKJ5H2ZOQRNT3ZKYMHZHJWIL/action/replication_record"}},"created_at":"2026-05-18T00:57:00.504827+00:00","updated_at":"2026-05-18T00:57:00.504827+00:00"}