Resource Allocation for Federated Knowledge Distillation Learning in Internet of Drones

Abstract

The Internet of Drones (IoD) integrates drone technology with the Internet of Things, enabling efficient data collection and communication applications. Federated learning (FL) in IoD networks facilitates collaborative model training while preserving data privacy but imposes significant computational and communication demands on resource-constrained drones. Federated knowledge distillation learning (FedKD) addresses this challenge by training both a large teacher model and a smaller student model locally but only updating the smaller student model, thereby reducing communication overhead. This article tackles the resource allocation problem in FedKD within IoD networks, focusing on optimizing CPU computing resource, wireless transmission power, and bandwidth allocation to minimize overall drone energy consumption. We formulate this as an optimization problem, considering constraints on latency, computing resource, bandwidth, and power. To effectively address this problem, we design a low-complexity algorithm. Extensive simulations validate our approach, showing it reduces energy consumption by an average of 85% compared to FedKD and 94% compared to FedAvg (a standard FL algorithm).