Federated Multi-Task Clustering

doi:10.1109/tcsvt.2026.3735054

Federated Multi-Task Clustering

2026-09-18

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DOI：10.1109/tcsvt.2026.3735054

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SSpectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models are designed for centralized settings, rendering them inapplicable in modern decentralized environments. Moreover, current federated learning approaches often suffer from poor generalization performance due to reliance on unreliable pseudo-labels, and fail to capture the latent correlations amongst heterogeneous clients. To tackle these limitations, this paper proposes a novel framework named Federated Multi-Task Clustering (i.e., FMTC), which intends to learn personalized clustering models for heterogeneous clients while collaboratively leveraging their shared underlying structure without transmitting raw local data. More specifically, the FMTC framework is composed of two main components: client-side personalized clustering module, which learns a parameterized mapping model to support robust out-of-sample inference, bypassing the need for unreliable pseudo-labels; and server-side tensorial correlation module, which explicitly captures the shared knowledge across all clients. This is achieved by organizing all client models into a unified tensor and applying low-rank regularization to discover their common subspace. To solve this joint optimization problem, we derive an efficient distributed ADMM algorithm based on the Alternating Direction Method of Multipliers, which decomposes the global problem into parallel local updates on clients and an aggregation step on the server. To this end, extensive experiments on real-world datasets demonstrate that our proposed FMTC framework significantly outperforms various baseline and state-of-the-art federated clustering algorithms. The reported protocol keeps raw local data at the clients, but it does not by itself provide a formal privacy guaranty against inference attacks.

Keywords:

Federated learning

unsupervised learning

spectral clustering

multi-task learning

tensor methods

Federated Multi-Task Clustering

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