Adaptive Compression for Trusted Data Circulation of Federated Learning in Internet of Vehicles

In the Internet of Vehicles (IoV), federated learning (FL) has been regarded as a promising solution for training distributed models while preserving data privacy, which is a critical requirement for trusted data circulation (TDC). However, due to the limited resources and the dynamic network structure of IoV, improving the data communication efficiency in FL training is still a huge challenge. Compressive sensing (CS) reduces communication overhead by enabling efficient data compression and reconstruction. In this paper, we take CS-enabled FL one step further by introducing an adaptive scheme to enhance communication performance. Specifically, we first select vehicles participating in FL based on moving conditions to maximize system stability and model convergence. Second, inspired by synaptic intelligence (SI) in the field of continuous learning (CL), we prioritize gradient information based on its importance in model performance to optimize the CS compression and reconstruction process. Experimental results show that the proposed scheme can achieve the same accuracy as the classical compression method with less or even less than half of the data, which indicates that it is a practical FL scheme for IoV.