2026
FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
CVPR 2026
Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in both data and resources makes this assumption impractical, thus motivating model-heterogeneous FL. To address