Unmanned Aerial Vehicle-Assisted Federated Learning Method Based on a Trusted Execution Environment

Abstract

In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into UAV-assisted federated learning and proposes a robust aggregation algorithm based on cosine distance, denoted as CosAvg. This study further designs and evaluates a TEE-based federated learning model, comparing its resource overhead with other secure aggregation frameworks, like homomorphic encryption (HE) and differential privacy (DP). Experimental results indicate a significant reduction in resource overhead for TEE against DP and HE. Moreover, the proposed CosAvg algorithm demonstrated superior robustness against adversarial scenarios, maintaining high accuracy in the presence of malicious clients. The integration of TEE and the CosAvg algorithm provides a secure and robust solution for UAV-assisted federated learning, effectively defending both gradient inversion attacks and byzantine attacks.