CCFC++: Enhancing Federated Clustering through Feature Decorrelation

Understand

In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data.

• This field has seen notable advancements through its marriage with contrastive learning, exemplified by Cluster-Contrastive Federated Clustering (CCFC).
• However, CCFC suffers from heterogeneous data across clients, leading to poor and unrobust performance.
• Our study conducts both empirical and theoretical analyses to understand the impact of heterogeneous data on CCFC.