Federated deep embedded clustering under privacy protection

Deep clustering, a prominent research focus in data mining, utilizes deeply embedded features to reveal the intrinsic statistical structure of data. However, existing deep clustering models rely on centralized datasets, which are impractical in scenarios with data silos and privacy constraints, leading to degraded clustering performance. Considering the privacy protection characteristics of federated learning, this paper incorporates the idea of federated learning into deep clustering, and proposes a federated deep embedding clustering (FDEC) model under privacy protection. FDEC follows a universal client-server structure, coordinating training between clients through a central server to obtain a unified central model. The server updates the global deep embedding and cluster centers based on a hybrid federated averaging strategy, while each client conducts two-stage deep clustering on local data without sharing raw data. To enhance robustness under non-independent and identically distributed (non-IID) conditions, the hybrid strategy improves parameter aggregation effectiveness. Experimental results on both IID and non-IID datasets demonstrate that FDEC is more robust and consistently outperforms centralized deep clustering methods.