Federated Multi-Task Clustering for Decentralized Data

This paper addresses the challenge of clustering in decentralized environments, where data privacy is a concern. It proposes a novel framework, FMTC, that combines personalized clustering models for heterogeneous clients with a server-side module to capture shared knowledge. The use of a parameterized mapping model avoids reliance on unreliable pseudo-labels, and the low-rank regularization on a tensor of client models is a key innovation. The paper's contribution lies in its ability to perform effective clustering while preserving privacy and accounting for data heterogeneity in a federated setting. The proposed algorithm, based on ADMM, is also a significant contribution.

• •Proposes FMTC, a novel federated clustering framework.
• •Employs personalized clustering models for heterogeneous clients.
• •Uses a server-side tensorial correlation module to capture shared knowledge.
• •Avoids reliance on unreliable pseudo-labels.
• •Demonstrates superior performance compared to existing methods.