Research on Edge-Cloud Collaborative Data Sharing Method Based on Federated Learning in Internet of Vehicles

With the development of Internet of Vehicles(IoV) technology and the popularization of intelligent vehicles, a large amount of vehicle data is generated and accumulated. Due to the privacy and sensitivity of vehicle datas, the emergence of Federated Learning(FL) technology can effectively protect user privacy. FL allows model training without the need to share raw data, protecting vehicle user data by sharing model parameters. However, within conventional FL methodologies, vehicle users are required to undertake significant computational tasks, and communication with cloud servers demands considerable time. Inspired by edge computing, we introduce a cloud-edge-end two-tier architecture based on federated learning(CEE-FLA).This structure transfers the model aggregation process originally completed by the cloud server to the edge server to reduce the number of communications with the cloud server, thereby improving the accuracy and efficiency of data sharing. Considering that model training efficiency is affected by vehicle performance, we improve the traditional FL algorithm in CEE-FLA and adopt a K-Vehicle random scheduling strategy to minimize the impact of vehicle performance differences on model convergence and data sharing efficiency. The experimental results demonstrate that our proposed CEE-FAL offers improvements in both accuracy and efficiency compared to the traditional cloud-end single-tier. Additionally, as the number of iterations between vehicles and edge servers increases, the model’s convergence speed also improves.