Networked Federated Learning-based Intelligent Vehicular Traffic Management in IoV Scenarios

With recent advancements in Internet of Things (IoT), Machine Learning (ML), and wireless communication networks, e.g., 6G, there has been an increasing interest in Intelligent Vehicular Networks (IVNs), where efficient traffic flow management is one of the most important requirements. In recent times, several new distributed ML methods have been introduced aiming at solving complex networking problems due to their added advantages in terms of learning efficiency and privacy over distributed wireless scenarios. With a focus on the upcoming 6G enabled Intelligent Transportation Systems, real-time traffic flow management is essential for providing the adequate Quality of Service to the end users. Distributed Learning methods can be crucial for solving the traffic management problem in highly dynamic vehicular systems. With this motivation, in this work, we have considered a distributed learning method, belonging to the class of the collaborative Federated Learning (FL) approaches, named Networked FL (NFL) for estimating the dynamic traffic flow over time and space. We have exploited the added advantage of NFL in terms of multi-task FL capabilities for predicting traffic patterns over different times and locations in a service area. The simulation results compared with the traditional centralized FL and independent area-based learning show improvements in terms of learning efficiency, accuracy, and the cost required.