Data heterogeneity across clients in federated learning (FL) settings is a widely acknowledged challenge. In response, personalized federated learning (PFL) emerged as a framework to curate local models for clients' tasks. In PFL, a common strategy is to develop local and global models jointly - the global model (for generalization) informs the local models, and the local models (for personalization) are aggregated to update the global model. A key observation is that if we can improve the generalization ability of local models, then we can improve the generalization of global models, which in turn builds better personalized models. In this work, we consider class imbalance, an overlooked type of data heterogeneity, in the classification setting. We propose FedNH, a novel method that improves the local models' performance for both personalization and generalization by combining the uniformity and semantics of class prototypes. FedNH initially distributes class prototypes uniformly in the latent space and smoothly infuses the class semantics into class prototypes. We show that imposing uniformity helps to combat prototype collapse while infusing class semantics improves local models. Extensive experiments were conducted on popular classification datasets under the cross-device setting. Our results demonstrate the effectiveness and stability of our method over recent works.

翻译：联邦学习（FL）中客户端间的数据异质性是一个公认的挑战。为此，个性化联邦学习（PFL）框架应运而生，旨在为客户端任务定制本地模型。在PFL中，常见策略是联合训练本地模型与全局模型——全局模型（负责泛化）指导本地模型，而本地模型（负责个性化）通过聚合更新全局模型。关键观察表明：若能提升本地模型的泛化能力，则可增强全局模型的泛化性，进而构建更优的个性化模型。本文聚焦分类场景中被忽视的异质性类型——类别不平衡问题。我们提出FedNH方法，通过融合类别原型的均匀性与语义信息，提升本地模型在个性化与泛化两方面的性能。FedNH首先在潜在空间均匀分布类别原型，随后将类别语义平滑注入原型。实验证明，施加均匀性可防止原型坍塌，而注入类别语义能优化本地模型。我们在跨设备场景下的主流分类数据集上进行了大量实验，结果验证了本方法相较近期工作的有效性与稳定性。