  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.
