Report
A Survey on Cluster-based Federated Learning
| العنوان: | A Survey on Cluster-based Federated Learning |
|---|---|
| المؤلفون: | El-Rifai, Omar, Ali, Michael Ben, Megdiche, Imen, Peninou, André, Teste, Olivier |
| سنة النشر: | 2025 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2501.17512 |
| رقم الانضمام: | edsarx.2501.17512 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |