Computer Science > Machine Learning
[Submitted on 12 Mar 2023 (v1), last revised 29 Mar 2024 (this version, v4)]
Title:Making Batch Normalization Great in Federated Deep Learning
View PDF HTML (experimental)Abstract:Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, prior work has observed that training with BN could hinder performance and suggested replacing it with Group Normalization (GN). In this paper, we revisit this substitution by expanding the empirical study conducted in prior work. Surprisingly, we find that BN outperforms GN in many FL settings. The exceptions are high-frequency communication and extreme non-IID regimes. We reinvestigate factors that are believed to cause this problem, including the mismatch of BN statistics across clients and the deviation of gradients during local training. We empirically identify a simple practice that could reduce the impacts of these factors while maintaining the strength of BN. Our approach, which we named FIXBN, is fairly easy to implement, without any additional training or communication costs, and performs favorably across a wide range of FL settings. We hope that our study could serve as a valuable reference for future practical usage and theoretical analysis in FL.
Submission history
From: Hong-You Chen [view email][v1] Sun, 12 Mar 2023 01:12:43 UTC (6,784 KB)
[v2] Wed, 1 Nov 2023 16:05:16 UTC (8,267 KB)
[v3] Tue, 13 Feb 2024 07:32:47 UTC (9,183 KB)
[v4] Fri, 29 Mar 2024 03:37:04 UTC (9,185 KB)
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