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arXiv · 2004.01442

From Local SGD to Local Fixed-Point Methods for Federated Learning

Grigory Malinovsky·Dmitry Kovalev·Elnur Gasanov·Laurent Condat·Peter Richtárik
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Abstract

Most algorithms for solving optimization problems or finding saddle points of convex-concave functions are fixed-point algorithms. In this work we consider the generic problem of finding a fixed point of an average of operators, or an approximation thereof, in a distributed setting. Our work is motivated by the needs of federated learning. In this context, each local operator models the computations done locally on a mobile device. We investigate two strategies to achieve such a consensus: one based on a fixed number of local steps, and the other based on randomized computations. In both cases, the goal is to limit communication of the locally-computed variables, which is often the bottleneck in distributed frameworks. We perform convergence analysis of both methods and conduct a number of experiments highlighting the benefits of our approach.

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Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik. 2020-06-16. From Local SGD to Local Fixed-Point Methods for Federated Learning. https://arxiv.org/abs/2004.01442

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