Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning

Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik

Keywords: Federated Learning, Compression, Sparsity, Pruning, Communication Efficiency, Local Training

TL;DR: This work presents Sparse-ProxSkip, a new method in Federated Learning that combines sparse training and acceleration to address client resource constraints and communication costs.

Abstract: In the recent paradigm of Federated Learning (FL), multiple clients train a shared model while keeping their local data private. Resource constraints of clients and communication costs pose major problems for training large models in FL. On the one hand, addressing the resource limitations of the clients, sparse training has proven to be a powerful tool in the centralized setting. On the other hand, communication costs in FL can be addressed by local training, where each client takes multiple gradient steps on its local data. Recent work has shown that local training can provably achieve the optimal accelerated communication complexity [Mishchenko et al., 2022]. Hence, one would like an accelerated sparse training algorithm. In this work we show that naive integration of sparse training and acceleration on the server fails, and how to fix it by letting the clients perform these tasks appropriately. We introduce Sparse-ProxSkip, our method developed for the nonconvex setting, inspired by RandProx [Condat and Richtárik, 2022], which provably combines sparse training and acceleration in the convex setting. We demonstrate the good performance of Sparse-ProxSkip in extensive experiments.

Primary Area: optimization

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Submission Number: 400