论文标题
车辆网络中的联合学习
Federated Learning in Vehicular Networks
论文作者
论文摘要
机器学习（ML）最近在车辆网络中采用了用于自动驾驶，道路安全预测和车辆对象检测等应用，这是由于其无模型的特性，从而允许自适应快速响应。但是，这些ML应用程序中的大多数采用集中学习（CL），这为参数服务器和车辆边缘设备之间的数据传输带来了重要的开销。最近已将联合学习（FL）框架作为一种有效的工具引入，目的是通过传输模型更新而不是整个数据集来减少传输开销，同时实现隐私。在本文中，我们调查了FL在车辆网络应用中的用法来开发智能运输系统。我们对FL对基于ML的车辆应用的可行性进行了全面分析，并通过利用基于图像的数据集作为案例研究来研究对象检测。然后，我们从学习角度（即数据标签和模型培训）以及通信的角度（即数据速率，可靠性，传输开销，隐私和资源管理）确定了主要挑战。最后，我们重点介绍了车辆网络中FL的未来研究指示。
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.
