MFC: Mixed Federated Clustering based on Cross-modal Feature Decoupling

Existing federated clustering methods typically assume that either all clients supply the same type of single-modal/multi-modal data, or that different clients provide various modalities describing the same object. However, a prevalent real-world scenario involves clients contributing data from diverse and unrelated modalities. Addressing the challenge of uncovering clustering patterns from such heterogeneous modality data distributed across distinct clients is crucial. In this paper, we propose a novel cross-modal feature decoupling-based mixed federated clustering model. To address client heterogeneity, we introduce a cross-modal feature decoupling module for each client, designed to decouple modality-agnostic and modality-specific features through distinct encoders.Only the modality-agnostic encoder parameters and clustering centers of each client are transmitted to the server. This enables the server to aggregate various modality-agnostic encoders, effectively discovering the global clustering structure while avoiding interference from modality-specific noise. Moreover, we develop a global consistent complementary clustering module to integrate the complementary clustering centers from various clients. The global clustering centers are then dispatched back to clients to guide and calibrate their local clustering models. Experimental results on three public datasets show the superiority of the proposed model compared to classic federated clustering methods.