A Survey on Clustered Federated Learning: Taxonomy, Analysis and Applications

Abstract

As Federated Learning (FL) expands, the challenge of non-independent and identically distributed (non-IID) data becomes critical. Clustered Federated Learning (CFL) addresses this by training multiple specialized models, each representing a group of clients with similar data distributions. However, the term ”CFL” has increasingly been applied to operational strategies unrelated to data heterogeneity, creating significant ambiguity. This survey provides a systematic review of the CFL literature and introduces a principled taxonomy that classifies algorithms into Server-side, Client-side, and Metadata-based approaches. Our analysis reveals a distinct dichotomy: while theoretical research prioritizes privacy-preserving Server/Client-side methods, applied papers overwhelmingly favor Metadata-based approaches, prioritizing efficiency over privacy. Furthermore, we explicitly distinguish ”Core CFL” (grouping clients for non-IID data) from ”Clustered X FL” (operational variants for system heterogeneity). Finally, we outline lessons learned and future directions to bridge the gap between theoretical privacy and practical efficiency.