Federated Learning for Smart Mobility: Towards Secure, Efficient, and Sustainable Transportation explores how federated learning (FL) reshapes the future of intelligent transportation and the Internet of Things (IoT). As data privacy and communication efficiency become pressing challenges, FL offers a distributed and privacy-preserving paradigm for model training across vehicles, sensors, and edge devices without sharing raw data.

This SpringerBrief provides a concise yet comprehensive overview of FL s role in building next-generation smart mobility systems. It covers the...