Accelerating Wireless Hierarchical Split Federated Learning via Smashed Data Generation
As the size of machine learning models scales up, conventional federated learning (FL) encounters challenges on wireless edge clients due to scarce resources. Split federated learning (SFL) mitigates these constraints by offloading part of the model to the edge server. However, most prior work adopts two-tier synchrono...