As a new paradigm that integrates clustering with federated learning, federated clustering (FC) has recently attracted increasing attention, as it addresses the practical issue of privacy protection in distributed data. In this paper, we provide a comprehensive survey of recent advances in FC. This survey is organized into four parts. First, since FC is often developed by extending existing clustering methods, we review several classical clustering paradigms. Meanwhile, the inherent challenges of FC are summarized, and common improvement strategies are categorized. Second, we summarize experimental setups and evaluation protocols used in FC studies. Third, from the perspectives of data partitioning schemes and whether deep representation learning is incorporated, FC methods are divided into four categories, and representative algorithms in each category are reviewed. Finally, we discuss the limitations of current FC approaches and highlight potential directions for future research.
Ding et al. (Thu,) studied this question.