Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.

翻译：个性化联邦学习是联邦学习中应对统计异质性并支持客户端自适应的重要方法之一。许多个性化联邦学习方法将模型划分为共享参数和个性化参数，并在每个客户端上联合训练这两类参数。然而，这会产生一个优化问题：共享参数由优化不同局部目标的客户端更新，可能导致共享更新不一致，从而削弱共享表示。为应对这一问题，我们提出联邦共享参数校正方法（FedSPC），这是一种用于个性化联邦学习的模块化校正方法。FedSPC仅对给定个性化联邦学习方法中的共享参数应用控制变量校正，而保持个性化参数不变。该方法可集成至三种常见的个性化联邦学习设置：共享特征提取器、共享分类器以及带局部正则化的完全共享模型。在基于ViT、ResNet-34和VGG-11的CIFAR-100和Tiny-ImageNet数据集上的实验表明，FedSPC能够提升包括FedPer、FedRep、FedBABU、LG-FedAvg和Ditto在内的代表性个性化联邦学习方法的性能。