Personalized Federated Learning for Statistical Heterogeneity
It addresses data privacy and model performance problems for federated learning practitioners, but is incremental as it reviews existing work.
This paper reviews personalized federated learning (PFL) to tackle statistical heterogeneity in federated learning, which causes issues like inadequate personalization and slow convergence, by summarizing current research progress, techniques, and challenges.
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.