Federated Learning for IoV Adaptive Vehicle Clusters: A Dynamic Gradient Compression Strategy

Federated learning (FL) has been extensively utilized in distributed learning scenarios for the Internet of Vehicles (IoV). However, two key challenges exist: 1) gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants and 2) the fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132- and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB.