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.

翻译：联邦学习（FL）的普及度日益提升，同时人工智能应用中对数据隐私的关注也与日俱增。FL在保障数据机密性的前提下，促进了多方协同模型学习。然而，由客户端数据分布差异导致的统计异质性问题引发了某些挑战，例如个性化不足和收敛缓慢。针对上述问题，本文简要总结了当前个性化联邦学习（PFL）领域的研究进展，概述了PFL的概念，分析了相关技术，并重点介绍了当前的研究工作。此外，本文还探讨了PFL潜在的未来研究方向及相关挑战。