Federated Learning in Vehicular Networks
This work addresses the problem of high data transmission costs and privacy concerns in machine learning for vehicular networks, but it is incremental as it reviews and analyzes existing FL concepts without introducing novel methods.
The paper investigates the use of federated learning (FL) over centralized learning (CL) in vehicular networks to reduce transmission overhead and enhance privacy for applications like autonomous driving and object detection, providing a comprehensive analysis and identifying key challenges without presenting new experimental results.
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data transmission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.