Georg Carle

FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillation-based FL, each client applies its local model on an unlabeled public dataset, and shares only prediction results wit...