{"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/powersgd-practical-low-rank-gradient","title":"PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization","arxiv_id":"1905.13727","date":"2019-05-31","proceeding":"NeurIPS 2019 12","authors":["Thijs Vogels","Sai Praneeth Karimireddy","Martin Jaggi"],"abstract":"We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test accuracy. 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