Towards Secure AI-empowered Vehicular Networks: A Federated Learning Approach using Homomorphic Encryption

Federated Learning (FL) offers a privacy-preserving approach to training machine learning models from distributed data on resource-constrained devices. However, even for modern/high-end cars vehicular networks, onboard training with the whole of the raw data presents challenges due to limited computing capability and power consumption concerns. To address this conundrum, we propose a novel FL framework that leverages homomorphic encyption (HE) to allow vehicles to upload encrypted portions of their data to a cloud server. Thanks to the key feature of HE, the server can perform additional model updates directly on the encrypted data, alleviating the workload on vehicles while preserving privacy. Furthermore, model updates from vehicles are also HE-encrypted, guaranteeing end-to-end privacy protection. This approach reduces the computational burden on vehicles while maintaining the model quality and convergence performance of the FL framework. Additionally, it can mitigate biases stemming from heterogeneous data, resulting in more stable FL convergence. Extensive experiments demonstrate the effectiveness of our framework in reducing workload and improving learning stability in vehicular networks.