解决个性化联邦学习中共享参数冲突问题，提升模型性能。
FedSPC: Shared Parameter Correction for Personalized Federated Learning
- 仅对共享参数施加控制变量修正，不改变个性化部分。
- 在多个数据集和模型上，显著提升现有个性化联邦学习方法表现。
- 适用于多种主流个性化联邦学习框架，易集成且效果稳定。
个性化联邦学习（PFL）是应对统计异质性并实现客户端自适应的重要方法。许多PFL方法将模型分为共享与个性化参数，在客户端联合训练。然而，这带来优化问题：各客户端基于不同局部目标更新共享参数，导致共享更新不一致，削弱共享表示能力。为此，我们提出联邦共享参数修正（FedSPC），一种模块化修正方法。FedSPC仅对特定PFL方法的共享参数应用控制变量修正，保持个性化参数不变。该方法可融入三种常见设置：共享特征提取器、共享分类器及全共享模型加本地正则化。在CIFAR-100与Tiny-ImageNet上，使用ViT、ResNet-34和VGG-11进行实验，结果显示FedSPC在代表性PFL方法（如FedPer、FedRep、FedBABU、LG-FedAvg、Ditto）上均有效提升性能。
原文摘要 · Abstract (English)
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.
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