Secure and Verifiable Edge-Federated Learning with Homomorphic Encryption and a Trusted Execution Environment for UAV Communication

To address the security challenges of untrusted servers and the difficulty in verifying malicious node identities in UAV swarm federated learning when relying solely on homomorphic encryption (HE), this paper proposes a verifiable HE-based federated learning (VHEFL) framework that integrates trusted execution environments (TEEs) with aggregate signatures. The method proceeds through a series of well-defined steps. Initially, each UAV computes model gradients locally, encrypts them using additive HE, and attaches a lightweight identity-based digital signature. The encrypted model gradients and signatures are uploaded to the server. Subsequently, the ground server performs gradient aggregation, including both the encrypted gradients and signatures, in the regular execution environment (REE). After performing aggregation in the REE, the aggregated ciphertext and all the signatures are sent to the TEE. Inside the TEE, an efficient aggregate verification algorithm is executed to batch-verify the authenticity and integrity of all signatures simultaneously. This enables the TEE to securely verify the aggregated result and return the aggregated model parameters to the UAVs, where the encrypted model parameters are decrypted locally to obtain the updated parameters. By combining low-overhead aggregate signature verification with hardware isolation, VHEFL provides a high-performance and verifiable security solution that effectively addresses the challenges of data privacy, malicious update prevention, and computational overhead in UAV swarm federated learning.