Bidirectional Adaptive Communication for Heterogeneous Distributed LearningDownload PDF

Published: 23 Nov 2022, Last Modified: 05 May 2023OPT 2022 PosterReaders: Everyone
Keywords: convex optimization, nonconvex optimization, stochastic optimization, distributed optimization, federated learning
TL;DR: Lazy, trigger-based communication between the server and the clients (in both directions)
Abstract: Communication is a key bottleneck in distributed optimization, and, in particular, bandwidth and latency can be limiting factors when devices are connected over commodity networks, such as in Federated Learning. State-of-the-art techniques tackle these challenges by advanced compression techniques or delaying communication rounds according to predefined schedules. We present a new scheme that adaptively skips communication (broadcast and client uploads) by detecting slow-varying updates. The scheme automatically adjusts the communication frequency independently for each worker and the server. By utilizing an error-feedback mechanism~-- borrowed from the compression literature~--~we prove that the convergence rate is the same as for batch gradient descent %strongly-convex, in the convex and nonconvex smooth cases. We show that the total number of communication rounds between server and clients needed to achieve a targeted accuracy is reduced, even in the case when the data distribution is highly non-IID.
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