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, IoV is transformed into an intelligent transportation system (ITS). There are, however, several operational considerations that hinder the adoption of ITSs, 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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为车联网和智能交通系统中的联邦学习提供案例


随着 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进步，新兴的网络技术和用例正在形成。其中一项技术是车联网 (IoV)，它描述了车辆和基础设施的互连系统。再加上人工智能和机器学习的最新发展，车联网转变为智能交通系统（ITS）。然而，有一些运营方面的考虑因素阻碍了 ITS 的采用，包括可扩展性、高可用性和数据隐私。为了应对这些挑战，建议采用联邦学习（一种协作式分布式智能技术）。通过 ITS 案例研究，强调了部署在整个网络路边基础设施上的联合模型通过利用群体智能从故障中恢复的能力，同时减少恢复时间并恢复可接受的系统性能。联合学习拥有众多用例和优势，是 ITS 的关键推动者，并有望在 5G 及其他网络和应用程序中实现广泛实施。