Federated Learning in Vehicular Networks

TL;DR

This paper explores federated learning as an efficient, privacy-preserving alternative to centralized machine learning in vehicular networks, analyzing its feasibility, challenges, and future research directions for intelligent transportation systems.

Contribution

It provides a comprehensive analysis of federated learning's application in vehicular networks, including case studies, challenges, and future research directions.

Findings

FL reduces data transmission overhead compared to CL.

Object detection performance using FL is promising.

Major challenges include data labeling, communication reliability, and resource management.

Abstract

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.