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Computer Science > Machine Learning

arXiv:1911.09030 (cs)
[Submitted on 20 Nov 2019 (v1), last revised 5 Dec 2020 (this version, v2)]

Title:Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates

Authors:Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, Haibin Lin
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Abstract:When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learning rates. We prove the convergence of the proposed algorithm for smooth but non-convex problems. Empirical results show that the proposed algorithm significantly reduces the communication overhead, which, in turn, reduces the training time by up to 30% for the 1B word dataset.
Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
Cite as: arXiv:1911.09030 [cs.LG]
  (or arXiv:1911.09030v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1911.09030

Submission history

From: Cong Xie [view email]
[v1] Wed, 20 Nov 2019 16:58:40 UTC (364 KB)
[v2] Sat, 5 Dec 2020 00:26:57 UTC (885 KB)
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