{"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/preserved-central-model-for-faster","title":"Preserved central model for faster bidirectional compression in distributed settings","arxiv_id":"2102.12528","date":"2021-02-24","proceeding":"NeurIPS 2021 12","authors":["Constantin Philippenko","Aymeric Dieuleveut"],"abstract":"We develop a new approach to tackle communication constraints in a distributed learning problem with a central server. We propose and analyze a new algorithm that performs bidirectional compression and achieves the same convergence rate as algorithms using only uplink (from the local workers to the central server) compression. To obtain this improvement, we design MCM, an algorithm such that the downlink compression only impacts local models, while the global model is preserved. As a result, and contrary to previous works, the gradients on local servers are computed on perturbed models. Consequently, convergence proofs are more challenging and require a precise control of this perturbation. To ensure it, MCM additionally combines model compression with a memory mechanism. This analysis opens new doors, e.g. incorporating worker dependent randomized-models and partial participation.","url_abs":"https://arxiv.org/abs/2102.12528v2","url_pdf":"https://arxiv.org/pdf/2102.12528v2.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":"preserved-central-model-for-faster","repo_url":"https://github.com/philipco/mcm-bidirectional-compression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"preserved-central-model-for-faster","repo_url":"https://github.com/philipco/artemis-bidirectional-compression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"model-compression","task_name":"Model Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.12528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12528"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/philipco/mcm-bidirectional-compression","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/philipco/artemis-bidirectional-compression","reach":{"status":"unanswered"}}],"summary":{"ran_violates":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":0,"samples":[{"code_sha256_prefix":"e6a79a6be3304f23","entry":"batch_step_size","repo":"philipco/mcm-bidirectional-compression","repo_kind":"official","path":"src/experiments_runner.py","file_url":"https://github.com/philipco/mcm-bidirectional-compression/blob/HEAD/src/experiments_runner.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e6a79a6be3304f23"}},{"code_sha256_prefix":"ed195ba6ad252639","entry":"iid_step_size","repo":"philipco/mcm-bidirectional-compression","repo_kind":"official","path":"src/experiments_runner.py","file_url":"https://github.com/philipco/mcm-bidirectional-compression/blob/HEAD/src/experiments_runner.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ed195ba6ad252639"}},{"code_sha256_prefix":"c178c850d8327c3d","entry":"deacreasing_step_size","repo":"philipco/mcm-bidirectional-compression","repo_kind":"official","path":"src/experiments_runner.py","file_url":"https://github.com/philipco/mcm-bidirectional-compression/blob/HEAD/src/experiments_runner.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c178c850d8327c3d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}