UAV-Enabled Split Learning With Privacy Preservation in Internet of Things

Deep learning-based applications have great potential for providing intelligent and personalized services in the Internet of Things (IoT). However, the resource limitation in IoT devices (IoTD) may significantly hinder deep learning applications in IoTs, especially when infrastructures are absent for critical environments. Unmanned aerial vehicle (UAV)-based split learning can alleviate this problem, by offloading the major part of the deep learning training tasks from IoTD to the unmanned aerial vehicle (UAV). Whereas, current studies often overlook the latent privacy challenges caused by the UAV and extra data transmissions. To address this issue while ensuring efficient split learning, we propose a novel privacy-preserving split learning architecture. Based on this architecture, we present an improved pipeline scheme to synchronize the training and communicating period between the UAV and the IoT device. Then, in the context of privacy preservation, we formulate an optimization model to minimize the system energy consumption by jointly optimizing model split points, UAV service slot allocation, and flight trajectories. Based on block coordinate descent (BCD) and successive convex approximation (SCA), we put forward HOTSS algorithm to find the optimized solution of this model. Simulation results show the fluctuating characteristic of energy consumption changed with the increase of the privacy preservation requirement, and show our approach can reduce overall system energy consumption by an average of 6.7% compared to the benchmark scheme.