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arXiv · 2109.02388

On Second-order Optimization Methods for Federated Learning

Sebastian Bischoff·Stephan Günnemann·Martin Jaggi·Sebastian U. Stich
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Abstract

We consider federated learning (FL), where the training data is distributed across a large number of clients. The standard optimization method in this setting is Federated Averaging (FedAvg), which performs multiple local first-order optimization steps between communication rounds. In this work, we evaluate the performance of several second-order distributed methods with local steps in the FL setting which promise to have favorable convergence properties. We (i) show that FedAvg performs surprisingly well against its second-order competitors when evaluated under fair metrics (equal amount of local computations)-in contrast to the results of previous work. Based on our numerical study, we propose (ii) a novel variant that uses second-order local information for updates and a global line search to counteract the resulting local specificity.

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Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich. 2021-09-06. On Second-order Optimization Methods for Federated Learning. https://arxiv.org/abs/2109.02388

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