  The popularity of federated learning (FL) is on the rise, along with growing
concerns about data privacy in artificial intelligence applications. FL
facilitates collaborative multi-party model learning while simultaneously
ensuring the preservation of data confidentiality. Nevertheless, the problem of
statistical heterogeneity caused by the presence of diverse client data
distributions gives rise to certain challenges, such as inadequate
personalization and slow convergence. In order to address the above issues,
this paper offers a brief summary of the current research progress in the field
of personalized federated learning (PFL). It outlines the PFL concept, examines
related techniques, and highlights current endeavors. Furthermore, this paper
also discusses potential further research and obstacles associated with PFL.
