BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles
Yongqiang Peng, Zongyao Chen, Zexuan Chen, Wei Ou, Wenbao Han, Jianqiang Ma
Abstract
Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.