2 papers
cs.LG2026
DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Mariona Jaramillo-Civill, Peng Wu, Pau Closas
Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a glo…
cs.LG2024
A Bayesian Framework for Clustered Federated Learning
Peng Wu, Tales Imbiriba, Pau Closas
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced d…