{"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/cocoa-a-general-framework-for-communication","title":"CoCoA: A General Framework for Communication-Efficient Distributed Optimization","arxiv_id":"1611.02189","date":"2016-11-07","proceeding":null,"authors":["Virginia Smith","Simone Forte","Chenxin Ma","Martin Takac","Michael. I. Jordan","Martin Jaggi"],"abstract":"The scale of modern datasets necessitates the development of efficient\ndistributed optimization methods for machine learning. We present a\ngeneral-purpose framework for distributed computing environments, CoCoA, that\nhas an efficient communication scheme and is applicable to a wide variety of\nproblems in machine learning and signal processing. We extend the framework to\ncover general non-strongly-convex regularizers, including L1-regularized\nproblems like lasso, sparse logistic regression, and elastic net\nregularization, and show how earlier work can be derived as a special case. We\nprovide convergence guarantees for the class of convex regularized loss\nminimization objectives, leveraging a novel approach in handling\nnon-strongly-convex regularizers and non-smooth loss functions. The resulting\nframework has markedly improved performance over state-of-the-art methods, as\nwe illustrate with an extensive set of experiments on real distributed\ndatasets.","url_abs":"http://arxiv.org/abs/1611.02189v2","url_pdf":"http://arxiv.org/pdf/1611.02189v2.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":"cocoa-a-general-framework-for-communication","repo_url":"https://github.com/epfml/cola","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"cocoa-a-general-framework-for-communication","repo_url":"https://github.com/gingsmith/cocoa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"distributed-optimization","task_name":"Distributed Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.02189","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}