{"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/lag-lazily-aggregated-gradient-for","title":"LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning","arxiv_id":"1805.09965","date":"2018-05-25","proceeding":"NeurIPS 2018 12","authors":["Tianyi Chen","Georgios B. Giannakis","Tao Sun","Wotao Yin"],"abstract":"This paper presents a new class of gradient methods for distributed machine\nlearning that adaptively skip the gradient calculations to learn with reduced\ncommunication and computation. Simple rules are designed to detect\nslowly-varying gradients and, therefore, trigger the reuse of outdated\ngradients. The resultant gradient-based algorithms are termed Lazily Aggregated\nGradient --- justifying our acronym LAG used henceforth. Theoretically, the\nmerits of this contribution are: i) the convergence rate is the same as batch\ngradient descent in strongly-convex, convex, and nonconvex smooth cases; and,\nii) if the distributed datasets are heterogeneous (quantified by certain\nmeasurable constants), the communication rounds needed to achieve a targeted\naccuracy are reduced thanks to the adaptive reuse of lagged gradients.\nNumerical experiments on both synthetic and real data corroborate a significant\ncommunication reduction compared to alternatives.","url_abs":"http://arxiv.org/abs/1805.09965v2","url_pdf":"http://arxiv.org/pdf/1805.09965v2.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":"lag-lazily-aggregated-gradient-for","repo_url":"https://github.com/chentianyi1991/LAG-code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.09965","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}