  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.
