  Federated learning is a distributed learning framework that takes full
advantage of private data samples kept on edge devices. In real-world federated
learning systems, these data samples are often decentralized and
Non-Independently Identically Distributed (Non-IID), causing divergence and
performance degradation in the federated learning process. As a new solution,
clustered federated learning groups federated clients with similar data
distributions to impair the Non-IID effects and train a better model for every
cluster. This paper proposes StoCFL, a novel clustered federated learning
approach for generic Non-IID issues. In detail, StoCFL implements a flexible
CFL framework that supports an arbitrary proportion of client participation and
newly joined clients for a varying FL system, while maintaining a great
improvement in model performance. The intensive experiments are conducted by
using four basic Non-IID settings and a real-world dataset. The results show
that StoCFL could obtain promising cluster results even when the number of
clusters is unknown. Based on the client clustering results, models trained
with StoCFL outperform baseline approaches in a variety of contexts.
