Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacerbate the class imbalanced problem. In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. Then we design a dynamic aggregation strategy to dynamically aggregate clients based on the affinity metric in each round to reduce the class imbalanced risk. Extensive experiments show that the proposed DA-PFL model can significantly improve the accuracy of each client in three real-world datasets with state-of-the-art comparison methods.

翻译：个性化联邦学习成为热门研究课题，可为每个客户端学习个性化模型。现有个性化联邦学习模型倾向于聚合具有相似数据分布的相似客户端，以提升学习模型性能。然而，基于相似性的个性化联邦学习方法可能加剧类别不平衡问题。本文提出一种新颖的基于动态亲和力的个性化联邦学习模型（DA-PFL），旨在缓解联邦学习过程中的类别不平衡问题。具体而言，我们从互补视角构建亲和力度量，用以指导哪些客户端应被聚合；进而设计动态聚合策略，每轮根据亲和力度量动态聚合客户端，以降低类别不平衡风险。大量实验表明，所提DA-PFL模型在三个真实数据集上相比最先进对比方法，能显著提升每个客户端的准确率。