Boosting Federated Learning Convergence with Prototype Regularization

• URL: http://arxiv.org/abs/2307.10575v1
• Date: Thu, 20 Jul 2023 04:35:50 GMT
• Title: Boosting Federated Learning Convergence with Prototype Regularization
• Authors: Yu Qiao, Huy Q. Le, Choong Seon Hong
• Abstract summary: This paper introduces a prototype-based regularization strategy to address the heterogeneous data distribution among clients. The experimental results on MNIST and Fashion-MNIST show that our proposal achieves improvements of 3.3% and 8.9% in average test accuracy.
• Score: 46.23872011375391
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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