Federated Learning with a Sampling Algorithm under Isoperimetry

• URL: http://arxiv.org/abs/2206.00920v1
• Date: Thu, 2 Jun 2022 08:19:03 GMT
• Title: Federated Learning with a Sampling Algorithm under Isoperimetry
• Authors: Lukang Sun, Adil Salim, Peter Richt\'arik
• Abstract summary: Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices. We propose a communication-efficient variant of Langevinvin's sample a posteriori.
• Score: 9.990687944474738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices, who own the training data. These techniques critically rely on reducing the communication cost -- the main bottleneck -- between the devices and a central server. Federated learning algorithms usually take an optimization approach: they are algorithms for minimizing the training loss subject to communication (and other) constraints. In this work, we instead take a Bayesian approach for the training task, and propose a communication-efficient variant of the Langevin algorithm to sample a posteriori. The latter approach is more robust and provides more knowledge of the \textit{a posteriori} distribution than its optimization counterpart. We analyze our algorithm without assuming that the target distribution is strongly log-concave. Instead, we assume the weaker log Sobolev inequality, which allows for nonconvexity.

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