V2V Communication-Assisted Federated Learning in Vehicular Networks

Federated learning (FL) enables vehicles to train machine learning (ML) models in a distributed manner for intelligent transportation system (ITS) services without sharing raw data, thereby preserving privacy and reducing communication overhead on roadside units (RSUs). However, due to their mobility and limited communication resources, vehicles participating in FL may be unable to transmit their updated models after local training if they move out of the RSU’s communication range. This limitation not only increases the convergence time of the global model but also wastes the computing resources of the participating vehicles used for training, as their trained models cannot be used to update the global model. To address these issues, this paper proposes a vehicle-to-vehicle communication-assisted FL (V2V-FL) mechanism for vehicular networks, in which vehicles moving out of the RSU’s coverage transmit their updated models to the RSU via a V2V link with limited communication resources, ensuring that more participating vehicles contribute their updated models to FL. An optimization problem for updated model forwarding with resource allocation is formulated to maximize the completion probability of participating vehicles while minimizing the total delay incurred during the training process. Since the formulated problem is a mixed-integer non-convex problem, it is decomposed into two subproblems, which are solved using the Karush-Kuhn-Tucker (KKT) conditions and a heuristic algorithm. Simulation results show that the proposed scheme outperforms other benchmark methods in terms of the total delay and convergence performance.