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arXiv · 1911.09030

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

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.

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Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, Haibin Lin. 2019-11-20. Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates. https://arxiv.org/abs/1911.09030

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