  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.
