Deep Reinforcement Learning-Based Resource Allocation for UAV-Enabled Federated Edge Learning

Abstract

The resource allocation of the federated learning (FL) for unmanned aerial vehicle (UAV) swarm systems are investigated. The UAV swarms based on FL realize the artificial intelligence (AI) applications by means of distributed training on the basis of ensuring the security of private data. However, the direct application of the FL in UAV swarms will incur high overhead. Therefore, in this article, we consider the resource allocation problem in FL for UAV swarms. To avoid the high communication overhead between UAVs and the central server, we proposed an FL framework for UAV swarms based on mobile edge computing (MEC) in which model aggregation is migrated to edge servers. In the proposed framework, the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption. In order to minimize the total cost of FL, we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning (DRL). The simulation result shows that: 1) compared with the benchmark algorithm, the proposed algorithm can effectively reduce the total cost of FL; 2) the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes.