Multi-View Clustering Goes Federated: A Survey
Abstract
The advent of contemporary data evolution has precipitated an unparalleled escalation in data generation, thereby necessitating the development of innovative methodologies to harness its latent potential. Federated Learning (FL) has emerged as a transformative paradigm, enabling collaborative model training across decentralized data sources while preserving privacy. This review explores the intersection of FL and Multi-View Clustering (MVC), investigating how these methodologies synergize to address the challenges of unsupervised learning in distributed environments. The present study examines the theoretical foundations, algorithmic advancements, and practical applications of MVC within the FL framework. The study under discussion highlights key contributions and identifies future research directions. Synthesizing insights from recent studies, this review aims to provide a comprehensive understanding of how decentralized learning can be effectively leveraged for MVC tasks, ultimately advancing the field of unsupervised learning in federated settings. Moreover, the ramifications of this integration for privacy-preserving Machine Learning (ML) and distributed systems are deliberated, with particular emphasis placed on the potential for innovative solutions to emerge from this convergence of methodologies.
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