Abstract

This article examines the integration of Federated Learning (FL) into big data analytics for intelligent transportation systems based on the Internet of Things (IoT). FL enables distributed machine learning model training without transferring sensitive data to a central server, preserving privacy and reducing data breach risks. The literature review highlights three key studies. The first demonstrates how FL improves traffic prediction accuracy using data from various sources, including vehicles and environmental sensors. The second introduces a big data architecture that integrates FL for real-time analysis and decision-making. The third emphasizes FL's role in sustainable traffic management, reducing congestion and carbon emissions through data-driven solutions. This article identifies research gaps and offers recommendations for optimizing FL in big data analytics, aiming to enhance efficiency, safety, and sustainability in modern transportation systems

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