A Survey on Cluster-based Federated Learning
arXiv:2501.17512v1 Announce Type: new
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.
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.
Omar El-Rifai (CIS-ENSMSE), Michael Ben Ali (IRIT), Imen Megdiche (IRIT, IRIT-SIG, INUC), Andr’e Peninou (IRIT, IRIT-SIG, UT2J), Olivier Teste (IRIT-SIG, IRIT, UT2J, UT)
Go to original source