Federated Learning for Smart Mobility - Jiaming Pei
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Présentation Federated Learning For Smart Mobility de Jiaming Pei Format Broché
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Résumé : 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&rsquo...
Biographie: Jiaming Pei is a Ph.D. candidate in Computer Science at the University of Sydney, Australia. His research focuses on federated learning, trustworthy AI, and their applications in intelligent transportation and IoT. He has published in prestigious journals including IEEE TITS, IEEE TCE, IEEE TAI, etc. Lukun Wang received his Ph.D. from the Ocean University of China in 2016. He is currently an Associate Professor and Master Supervisor at the School of Intelligent Equipment, Shandong University of Science and Technology. His research interests encompass artificial intelligence, computer vision, big data, and the Internet of Things. Dr. Wang also serves as an specially invited reviewer for the IEEE. Minghui Dai is a faculty member and Master&rsquo...
Sommaire:
s role in building next-generation smart mobility systems. It covers the fundamentals of FL and IoT infrastructures, introduces emerging applications in autonomous driving, traffic prediction, and vehicular networks, and presents selected case studies from academia and industry. The book also discusses key technical challenges&mdash...
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