Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities. Recently, personalized FL (pFL) has received attention for its ability to address statistical heterogeneity and achieve personalization in FL. However, from the perspective of feature extraction, most existing pFL methods only focus on extracting global or personalized feature information during local training, which fails to meet the collaborative learning and personalization goals of pFL. To address this, we propose a new pFL method, named GPFL, to simultaneously learn global and personalized feature information on each client. We conduct extensive experiments on six datasets in three statistically heterogeneous settings and show the superiority of GPFL over ten state-of-the-art methods regarding effectiveness, scalability, fairness, stability, and privacy. Besides, GPFL mitigates overfitting and outperforms the baselines by up to 8.99% in accuracy.

翻译：摘要：联邦学习因其隐私保护和协作学习能力而广受欢迎。近年来，个性化联邦学习因其能解决统计异质性并实现联邦学习个性化而备受关注。然而，从特征提取的角度看，现有大多数个性化联邦学习方法在本地训练过程中仅侧重于提取全局或个性化特征信息，这难以兼顾个性化联邦学习的协作学习与个性化目标。为此，我们提出一种名为GPFL的新型个性化联邦学习方法，旨在每个客户端上同步学习全局与个性化特征信息。我们在三种统计异质性设置下的六个数据集上进行了广泛实验，结果表明GPFL在有效性、可扩展性、公平性、稳定性和隐私性方面均优于十种最先进方法。此外，GPFL能有效缓解过拟合问题，其准确率相比基线方法最高提升8.99%。