A personalized federated cloud-edge collaboration framework via cross-client knowledge distillation

Shining Zhang, Xingwei Wang, Rongfei Zeng, Chao Zeng, Ying Li, Min Huang

Abstract: Highlights•Introduces FedCD, a novel framework for personalized federated learning.•Implements a local training strategy leveraging cross-client knowledge.•Designs a global weighted aggregation mechanism based on cross-client importance.•Provides convergence analysis and extensive experiments demonstrating the efficiency.