FEDL: Confidential Deep Learning for Autonomous Driving in VANETs Based on Functional Encryption

Deep learning is increasingly utilized in data-driven tasks in Vehicular Ad-hoc Networks (VANETs) such as traffic sign recognition or pedestrian detection, and is expected to fulfill autonomous driving. These applications involve large amounts of discrete private data. Federated learning can effectively address the issues of data islands and security. However, it is challenging to apply centralized framework to meet the rigorous requirements of intelligent services in VANETs, such as ultra-low latency, high security and reliability. In this work, we propose a privacy-preserving deep learning scheme named FEDL, which is built on a decentralized framework (abbreviated as EDVAN). This customized EDVAN structure decomposes computing tasks into each edge computing node equally. By combining the Voronoi diagram the network communication delay and overhead are alleviated tremendously. Furthermore, the FEDL scheme utilizes functional encryption to establish secure data communication and drop-allowance parameter aggregation module, achieving to resist inference attacks and eliminate the straggler effect. Finally, we apply the NS-3 simulator to simulate EDVAN’s communication environment. Three real traffic datasets are utilized to show the FEDL model is accurate lossless and efficient. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HuangZengyi/FEDL</uri>.