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.

翻译：作为一种分布式机器学习技术，联邦学习（FL）要求客户端在不泄露本地数据的情况下，与边缘服务器协作训练共享模型。然而，客户端间的异构数据分布常导致模型性能下降。为解决该问题，本文提出了一种基于原型正则化的策略以应对数据分布异质性。具体而言，该正则化流程包含服务器聚合分布式客户端的局部原型以生成全局原型，随后将其回传至各客户端以指导其本地训练。在MNIST与Fashion-MNIST数据集上的实验结果表明，与最常用基线FedAvg相比，本方案的平均测试准确率分别提升了3.3%与8.9%。此外，本方法在异构场景下具有快速收敛特性。