Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems
This addresses scalability and privacy issues for Internet of Vehicles and Intelligent Transportation Systems, but it is incremental as it applies an existing method to a new domain.
The paper tackles operational challenges like scalability and data privacy in Intelligent Transportation Systems by proposing Federated Learning, demonstrating through a case study that it reduces recovery time and restores system performance after faults.
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.