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

Edge drones continuously collect sensitive information such as telemetry data during missions, making it difficult to apply centralized model training directly due to privacy protection, security compliance, and regulatory constraints. Although federated learning (FL) can avoid sharing raw data, existing federated learning schemes based solely on homomorphic encryption (HE) still face security risks in drone scenarios, such as gradient inversion, member inference, and malicious update injection. To address this, we propose a secure and verifiable edge federated learning framework for parameter-efficient model adaptation in drone scenarios. The framework introduces homomorphic encryption for model updates on the device side to protect the privacy of updates before transmission and aggregation. Simultaneously, on the server side, decryption, aggregation, and verification are performed through a remotely authenticated Trusted Execution Environment (TEE), thereby limiting the server's access to plaintext updates and reducing the feasibility of gradient inversion and member inference attacks at the system level. Furthermore, an aggregation signature mechanism is introduced to batch verify the identity and update integrity of participating nodes, effectively preventing malicious or tampered updates from participating in aggregation, thus overcoming the shortcomings of existing HE-FL schemes in terms of poisoning resistance and verifiability. Experimental results show that, while ensuring safety and verifiability, the proposed method improves model accuracy by 3% compared to the comparative scheme, while maintaining better performance in terms of computation and communication overhead, thus verifying the practicality and deployability of the framework in resource-constrained UAV edge environments.