With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, the IoV is transformed into an Intelligent Transportation System (ITS). There are, however, several operational considerations that hinder the adoption of ITS systems, including scalability, high availability, and data privacy. To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested. Through an ITS case study, the ability of a federated model deployed on roadside infrastructure throughout the network to recover from faults by leveraging group intelligence while reducing recovery time and restoring acceptable system performance is highlighted. With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.

中文翻译：

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在车辆和智能交通系统的互联网中为联合学习提供依据

随着即将到来的5G网络的引入以及网络功能虚拟化和软件定义网络等技术的发展，新兴的新兴网络技术和用例正在形成。一种这样的技术是车辆互联网（IoV），它描述了车辆和基础设施的互连系统。结合人工智能和机器学习的最新发展，IoV已转变为智能运输系统（ITS）。但是，存在一些阻碍ITS系统采用的操作注意事项，包括可伸缩性，高可用性和数据隐私。为了应对这些挑战，建议使用联合学习和协作式智能技术“联合学习”。通过ITS案例研究，强调了在整个网络中部署在路边基础结构上的联合模型通过利用组智能，同时减少恢复时间和恢复可接受的系统性能，从故障中恢复的能力。凭借大量的用例和优势，联合学习是ITS的关键推动力，并有望在5G以及网络和应用之外实现广泛的实施。