Energy Efficient and Low Latency Federated Distillation Over UAV-Assisted Wireless Networks

Abstract

Unmanned aerial vehicles (UAVs) equipped with sensors, computing units, and communication modules, together with ground devices, constitute a ubiquitous integrated low-altitude network, which can provide users with sustainable computing and communication services in areas where terrestrial infrastructure has been compromised or rendered inoperable. Federated learning-enabled UAV (FL-UAV) wireless networks fully utilize the computational and communication capabilities of UAVs to protect user data privacy by exchanging model updates with ground devices. However, facing the challenges of low energy utilization efficiency and high training latency caused by UAV deployment, resource allocation, and communication overhead in FL-UAV. Existing solutions do not achieve efficient communication and resource scheduling to solve the energy and delay optimization issues in FL-UAV wireless networks. In this paper, we propose an air-to-ground integrated federated distillation (AirFD) framework for UAV-assisted mobile computing and communication networks, which significantly reduces communication overhead between UAV and ground devices by introducing knowledge distillation to transmit average logits instead of model parameters. Furthermore, we formulate cross-layer resource scheduling in AirFD as a non-convex optimization problem to achieve a trade-off between energy consumption and delay. To solve this nonlinear coupling and NP-complete problem, we use successive convex approximation and greedy algorithm to obtain the local optimal solution. Simulation evaluation and field experiments confirm the effectiveness of our proposed method in reducing communication costs and training delays by nearly 40%, and increasing energy utilization by about 50%.