Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning framework. Specifically, during each communication round, we group clients into multiple clusters based on their model training status and data distribution on the server side. We then consider each cluster center as a node equipped with model parameters and construct a graph that connects these nodes using weighted edges. Additionally, we update the model parameters at each node by propagating information across the entire graph. Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies. Experimental results demonstrate the effectiveness of the proposed work.

翻译：大多数现有的个性化联邦学习方法基于复杂的设计，通常需要繁琐的实现和调参。为解决这一局限性，我们提出一种简单而有效的个性化联邦学习框架。具体而言，在每个通信轮次中，我们根据客户端在服务器端的模型训练状态和数据分布将其分组为多个聚类。然后，我们将每个聚类中心视为一个配备模型参数的节点，并通过加权边构建连接这些节点的图。此外，我们通过在整个图上传播信息来更新每个节点的模型参数。随后，我们设计了一种精确的个性化模型分发策略，使客户端能够从服务器端获得最合适的模型。我们在三个图像基准数据集上进行了实验，并创建了具有三种拓扑类型的合成结构化数据集。实验结果证明了所提出方法的有效性。