A Novel Framework for Traffic Congestion Management at Intersections Using Federated Learning and Vertical Partitioning

Federated learning has shown exceptional usability in diverse domains, such as IoT, healthcare, FinTech, Insurance Sector and other Industries and Technologies. In numerous emerging VANET applications, integrating federated learning and vertically partitioned (VP) systems has yielded advantageous outcomes. There is still much to discover in the field of traffic management and intelligent mobility. The adaptation of federated learning in ITS makes it safer, efficient and enhances vehicular mobility. The majority of federated learning techniques for vertically partitioned data are synchronous in nature. The development of asynchronous training algorithms for VP data is crucial while maintaining the privacy of the data to increase efficiency. This paper presents a Privacy-Preserving Asynchronous Federated Learning and Vertical partitioning based Algorithm that reduces the duration of idling at red lights and improves the fuel consumption of ITS. Additionally, we have introduced a technique that enhances the passing-vehicle ratio at the intersection and efficiently manages the traffic. Using a mathematical approach, we showed the fuel consumption dependency over time for traveling and fuel consumption. In the end, we evaluated our proposed model using various parameters, which shows our model's efficiency. Our suggested model outperforms traditional vehicle passing per unit time ratio approaches. The ratio value provided by the traditional model is 0.88; however, the ratio value obtained by our proposed model sets a new benchmark, which is 1.33.