{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beer-fast-o-1-t-rate-for-decentralized","title":"BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression","arxiv_id":"2201.13320","date":"2022-01-31","proceeding":null,"authors":["Haoyu Zhao","Boyue Li","Zhize Li","Peter Richtárik","Yuejie Chi"],"abstract":"Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number of communication rounds. This paper proposes BEER, which adopts communication compression with gradient tracking, and shows it converges at a faster rate of $O(1/T)$. This significantly improves over the state-of-the-art rate, by matching the rate without compression even under arbitrary data heterogeneity. Numerical experiments are also provided to corroborate our theory and confirm the practical superiority of BEER in the data heterogeneous regime.","url_abs":"https://arxiv.org/abs/2201.13320v3","url_pdf":"https://arxiv.org/pdf/2201.13320v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beer-fast-o-1-t-rate-for-decentralized","repo_url":"https://github.com/liboyue/beer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.13320","atlas_url":"https://app.syntology.ai/?focus=2201.13320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.13320"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/liboyue/beer","reach":null}],"summary":{"ran_honours":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"b4de8359d066f1a8","entry":"flatten_tensors","repo":"liboyue/beer","repo_kind":"official","path":"beer/optim/BEER.py","file_url":"https://github.com/liboyue/beer/blob/HEAD/beer/optim/BEER.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b4de8359d066f1a8"}},{"code_sha256_prefix":"960cf8d02ec51e18","entry":"BEER","repo":"liboyue/beer","repo_kind":"official","path":"beer/optim/BEER.py","file_url":"https://github.com/liboyue/beer/blob/HEAD/beer/optim/BEER.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"960cf8d02ec51e18"}},{"code_sha256_prefix":"6a07bfbcba5cc212","entry":"CompressedOptimizer","repo":"liboyue/beer","repo_kind":"official","path":"beer/optim/BEER.py","file_url":"https://github.com/liboyue/beer/blob/HEAD/beer/optim/BEER.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6a07bfbcba5cc212"}},{"code_sha256_prefix":"888aadb499a36597","entry":"reduce_tensors","repo":"liboyue/beer","repo_kind":"official","path":"beer/optim/BEER.py","file_url":"https://github.com/liboyue/beer/blob/HEAD/beer/optim/BEER.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"888aadb499a36597"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}