In the contemporary landscape of intelligent vehicular systems, the challenge of ensuring robust data security while optimizing energy consumption remains a significant hurdle. This research delves into the potential of Federated Learning (FL) as a solution, providing a comprehensive exploration into its application within vehicular networks. The methodology adopted was rigorous, with a dual focus on designing a Federated Learning model tailored for vehicular systems and establishing a robust assessment framework. This framework critically evaluated security threats specific to FL in vehicular contexts and dissected energy consumption patterns. The findings were revelatory. When integrated adeptly, FL not only enhances data security by minimizing data centralization but also offers substantial reductions in energy consumption, primarily by curbing unnecessary data transmissions. These insights underscore the transformative potential of Federated Learning in vehicular systems, heralding a future where vehicles are not just smart but also collaborative, secure, and sustainable.