{"paper":{"title":"Communication Compression for Decentralized Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ce Zhang, Hanlin Tang, Ji Liu, Shaoduo Gan, Tong Zhang","submitted_at":"2018-03-17T01:51:09Z","abstract_excerpt":"Optimizing distributed learning systems is an art of balancing between computation and communication. There have been two lines of research that try to deal with slower networks: {\\em communication compression} for low bandwidth networks, and {\\em decentralization} for high latency networks. In this paper, We explore a natural question: {\\em can the combination of both techniques lead to a system that is robust to both bandwidth and latency?}\n  Although the system implication of such combination is trivial, the underlying theoretical principle and algorithm design is challenging: unlike centra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06443","kind":"arxiv","version":5},"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"}