Exploring Federated Optimization by Reducing Variance of Adaptive Unbiased Client Sampling

Dun Zeng, Zenglin Xu, Yu Pan, Qifan Wang, Xiaoying Tang

Keywords: Optimization; Federated Learning; Unbiased Sampling; Variance Reduction;

TL;DR: We propose using the independent sampling procedure in unbiased client sampling in federated optimization and present a novel adaptive unbiased client sampling for FL.

Abstract: Federated Learning (FL) systems usually sample a fraction of clients to conduct a training process. Notably, the variance of global estimates for updating the global model built on information from sampled clients is highly related to federated optimization quality. This paper explores a line of "free" adaptive client sampling techniques in federated optimization, where the server builds promising sampling probability and reliable global estimates without requiring additional local communication and computation. We capture a minor variant in the sampling procedure and improve the global estimation accordingly. Based on that, we propose a novel sampler called K-Vib, which solves an online convex optimization respecting client sampling in federated optimization. It achieves improved a linear speed up on regret bound $\tilde{\mathcal{O}}\big(N^{\frac{1}{3}}T^{\frac{2}{3}}/K^{\frac{4}{3}}\big)$ with communication budget $K$. As a result, it significantly improves the performance of federated optimization. Theoretical improvements and intensive experiments on classic federated tasks demonstrate our findings.

Primary Area: optimization

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Submission Number: 4540