Privacy-Preserving Decentralized Federated Multi-View Clustering

Abstract

In the era of big data, multi-view data exist in large quantities, and most existing multi-view clustering methods aggregate the data of all views for learning. However, data from different views are stored on different devices in some practical applications, and some of the data are private and cannot be shared. If the data of each view are regarded as nodes in a distributed network, these problems can be solved by introducing federated learning into multi-view clustering. Federated learning utilizes a central server for coordination; however, it becomes invalid when the central server is missing or faulty. This paper proposes a Decentralized Federated Multi-view Clustering (DFMC) approach to address this issue. First, the low-dimensional representation of each view is learned using Non-negative Matrix Factorization (NMF). Next, a consistency constraint is applied to the low-dimensional representations of different views based on the consistency of the view information. This constraint implements information communication between neighboring views and constructs a decentralized federated learning environment. Finally, a unified low-dimensional representation matrix is obtained and applied for clustering. Privacy preservation is achieved using the Alternating Minimization (AM) algorithm for individual views separately. Experimental results on real datasets verify the effectiveness and convergence of the DFMC approach.