Research on a fast-sharing solution for real-time traffic conditions based on federated learning

Federated learning has resolved the issue of privacy-protected data sharing among vehicles in vehicular network environments. However, traditional federated learning schemes suffer from long convergence times and significant network resource consumption. To fulfill the future needs for real-time traffic condition data and to alleviate the bandwidth pressure on backbone networks, this paper introduces a privacy-preserving federated learning scheme for rapid sharing of real-time traffic conditions. This system begins by designing a method that involves selecting highquality networking partners and optimizing the federated network structure of vehicles, thereby enhancing the efficiency with which vehicles obtain real-time traffic condition data. The results demonstrate that the data update efficiency of this system exceeds that of conventional vehicular network federated learning mechanisms. It provides multiple benefits, such as reduced potential attack windows, improved data timeliness, and lower communication costs.