Federated Learning (FL) allows collaborative machine learning training without sharing private data. Numerous studies have shown that one significant factor affecting the performance of federated learning models is the heterogeneity of data across different clients, especially when the data is sampled from various domains. A recent paper introduces variance-aware dual-level prototype clustering and uses a novel $\alpha$-sparsity prototype loss, which increases intra-class similarity and reduces inter-class similarity. To ensure that the features converge within specific clusters, we introduce an improved algorithm, Federated Prototype Learning with Convergent Clusters, abbreviated as FedPLCC. To increase inter-class distances, we weight each prototype with the size of the cluster it represents. To reduce intra-class distances, considering that prototypes with larger distances might come from different domains, we select only a certain proportion of prototypes for the loss function calculation. Evaluations on the Digit-5, Office-10, and DomainNet datasets show that our method performs better than existing approaches.

本研究针对联邦学习中不同客户端数据异构性导致的性能问题，提出了一种改进的算法——联邦原型学习聚类收敛(FedPLCC)。该方法通过引入加权原型和优化的原型损失函数，显著提高了模型的分类精度，尤其在多个数据集上的评估结果均优于现有方法，具有重要的应用潜力。

一种增强的联邦原型学习方法应对领域转移