Research Area:  Machine Learning
Machine learning (ML) has already been adopted in vehicular networks for such applications as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, the training of the ML model brings significant overhead for the data transmission between the parameter server and the edge devices in the vehicles. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing this transmission overhead while also achieving privacy through the transmission of only the model updates of the learnable parameters rather than the whole dataset. In this article, we investigate the usage of FL over ML 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. 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/delay, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.
Author(s) Name:  Ahmet M. Elbir, Burak Soner, Sinem Coleri
Journal name:  Signal Processing
Publisher name:  arXiv:2006.01412
Paper Link:   https://arxiv.org/abs/2006.01412