A Survey on Cluster-based Federated Learning

• URL: http://arxiv.org/abs/2501.17512v1
• Date: Wed, 29 Jan 2025 09:30:21 GMT
• Title: A Survey on Cluster-based Federated Learning
• Authors: Omar El-Rifai, Michael Ben Ali, Imen Megdiche, André Peninou, Olivier Teste,
• Abstract summary: In settings were Federated Learning clients' data is non-independently and identically distributed, the baseline FL approach seems to fall short. PFL relaxes the implicit single-model constraint and allows for multiple hypotheses to be learned from the data or local models. Among the personalized FL approaches, cluster-based solutions (CFL) are particularly interesting whenever it is clear -through domain knowledge -that the clients can be separated into groups.
• Score: 0.5242869847419834
• License:
• Abstract: As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms. In settings were FL clients' data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short. To tackle this issue, recent studies, have looked into personalized FL (PFL) which relaxes the implicit single-model constraint and allows for multiple hypotheses to be learned from the data or local models. Among the personalized FL approaches, cluster-based solutions (CFL) are particularly interesting whenever it is clear -through domain knowledge -that the clients can be separated into groups. In this paper, we study recent works on CFL, proposing: i) a classification of CFL solutions for personalization; ii) a structured review of literature iii) a review of alternative use cases for CFL. CCS Concepts: $\bullet$ General and reference $\rightarrow$ Surveys and overviews; $\bullet$ Computing methodologies $\rightarrow$ Machine learning; $\bullet$ Information systems $\rightarrow$ Clustering; $\bullet$ Security and privacy $\rightarrow$ Privacy-preserving protocols.

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