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

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