MVMAFOL: A Multi-Access Three-Layer Federated Online Learning Algorithm for Internet of Vehicles

With the development of intelligent transportation system, it is urgent to transmit and analyze traffic data based on Internet of Vehicles. Federated learning has the characteristics of privacy protection, distributed learning and making full use of the computing power of local devices, which is very suitable for IoV's streaming big data analysis. Classical federated learning has only one global model, thus it can't be applied directly to IoV, where different region may have different data distribution. It is preferable that model of different region can be trained separately. Besides, the high mobility of vehicle nodes allows them to enter other areas while collecting data. This paper tries to solve the above issues. Firstly, a new vehicle-edge-cloud three-layer federated learning architecture for Io V, the Mobile Vehicle Multiple Access Federated Online Learning (MVMAFOL) algorithm, is proposed, where each vehicle terminal can upload the local model to multiple edge servers to maximize the use of its local model and effectively improve the generalization ability of the global model. Besides, a Model Similarity Vehicle Multiple Access (MSVMA) algorithm is proposed to improve the performance of MVMAFOL in the regional model aggregation phase, and an Edge Cloud Parameter Amendment (ECPA) algorithm is proposed for the global model aggregation phase. Simulation results show that this scheme can effectively improve the accuracy of the trained model.