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arXiv · 2002.07839

Is Local SGD Better than Minibatch SGD?

Blake Woodworth·Kumar Kshitij Patel·Sebastian U. Stich·Zhen Dai·Brian Bullins·H. Brendan McMahan·Ohad Shamir·Nathan Srebro
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Abstract

We study local SGD (also known as parallel SGD and federated averaging), a natural and frequently used stochastic distributed optimization method. Its theoretical foundations are currently lacking and we highlight how all existing error guarantees in the convex setting are dominated by a simple baseline, minibatch SGD. (1) For quadratic objectives we prove that local SGD strictly dominates minibatch SGD and that accelerated local SGD is minimax optimal for quadratics; (2) For general convex objectives we provide the first guarantee that at least sometimes improves over minibatch SGD; (3) We show that indeed local SGD does not dominate minibatch SGD by presenting a lower bound on the performance of local SGD that is worse than the minibatch SGD guarantee.

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Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro. 2020-07-20. Is Local SGD Better than Minibatch SGD?. https://arxiv.org/abs/2002.07839

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