Robust Federated Multi-View Clustering

Federated Multi-View Clustering (FMVC) has attracted widespread attention in the field of privacy-preserving data analysis. However, existing methods usually suffer from noise interference in practical distributed scenarios: the raw data collected by different clients generally contain noise, outliers, and local distortions, which lead to unstable local representations and clustering structures, resulting in degraded clustering performance. To address the problem, we propose a robust federated multi-view clustering method (RFMVC). For each client, instead of learning clustering structure directly on the original noisy data, our method learns a denoised feature space and learns clustering structure in it to preserve the key structural information of the original data while effectively suppressing the influence of noise and local perturbations on clustering results. For the server, it employs the tensor Schatten p-norm to impose a unified constraint on the cluster indicator matrices of all clients, and combines it with an adaptive client weighting mechanism to fuse multi-client information, thereby enhancing cross-client consistency while achieving denoising at the global structural level. Experimental results show that the proposed method outperforms methods on multiple real-world multi-view datasets, which verifies its robustness.