Optimizing Privacy and Latency Tradeoffs in Split Federated Learning Over Wireless Networks

In this letter, a novel cut layer selection scheme is designed to minimize the overall latency in split federated learning (SFL) over wireless networks, while maintaining an acceptable privacy level. Considering a tradeoff between overall latency and privacy level in terms of the cut layer selection, we establish a theoretical framework for managing cut layer selection in SFL to optimize the cut layer point. Furthermore, we discuss the impact of a differential privacy technique designed to enhance privacy by effectively concealing individual information. We evaluate the performance of the proposed scheme and provide insights on optimizing the overall latency of SFL while maintaining the desired privacy level through cut layer selection.