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arXiv · 2307.10575

Boosting Federated Learning Convergence with Prototype Regularization

Yu Qiao·Huy Q. Le·Choong Seon Hong
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Abstract

As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data. However, the heterogeneous data distribution among clients often leads to a decrease in model performance. To tackle this issue, this paper introduces a prototype-based regularization strategy to address the heterogeneity in the data distribution. Specifically, the regularization process involves the server aggregating local prototypes from distributed clients to generate a global prototype, which is then sent back to the individual clients to guide their local training. The experimental results on MNIST and Fashion-MNIST show that our proposal achieves improvements of 3.3% and 8.9% in average test accuracy, respectively, compared to the most popular baseline FedAvg. Furthermore, our approach has a fast convergence rate in heterogeneous settings.

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BibTeXRIS

Yu Qiao, Huy Q. Le, Choong Seon Hong. 2023-07-20. Boosting Federated Learning Convergence with Prototype Regularization. https://arxiv.org/abs/2307.10575

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