Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems

TL;DR

This paper advocates for using federated learning in the Internet of Vehicles and Intelligent Transportation Systems to enhance scalability, privacy, and fault recovery, demonstrating its potential through a case study.

Contribution

It introduces federated learning as a solution for operational challenges in ITS, supported by a case study showing improved fault recovery and system performance.

Findings

Federated learning improves fault recovery in ITS.

Reduces data privacy concerns in vehicle networks.

Enhances system resilience and scalability.

Abstract

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.