Federated Learning for Smart Mobility: Towards Secure, Efficient, and Sustainable Transportation System (SpringerBriefs in Computer Science) - Softcover

Pei, Jiaming; Wang, Lukun; Dai, Minghui

Softcover

ISBN 10: 9819561590 ISBN 13: 9789819561599

Publisher: Springer, 2026

Synopsis

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 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—including data heterogeneity, system scalability, and privacy protection—and highlights future directions integrating FL with edge intelligence, 6G communication, and blockchain technologies.

Written by active researchers in the fields of federated learning, wireless communication, and intelligent transportation, this book serves as a valuable reference for scientists, graduate students, and professionals in AI, IoT, and smart city development. It bridges theoretical advances with practical insights, guiding readers toward secure, efficient, and sustainable mobility solutions.

About this title

About the Author

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’s supervisor at Donghua University, China. He received his Ph.D. from Shanghai University and was a postdoctoral researcher at the University of Macau. His research spans wireless communications, cloud-edge collaboration, and IoT security. Dr. Dai has published in leading IEEE journals and has received multiple Best Paper Awards from international conferences.

From the Back Cover

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 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—including data heterogeneity, system scalability, and privacy protection—and highlights future directions integrating FL with edge intelligence, 6G communication, and blockchain technologies.

Written by active researchers in the fields of federated learning, wireless communication, and intelligent transportation, this book serves as a valuable reference for scientists, graduate students, and professionals in AI, IoT, and smart city development. It bridges theoretical advances with practical insights, guiding readers toward secure, efficient, and sustainable mobility solutions.

 

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’s supervisor at Donghua University, China. He received his Ph.D. from Shanghai University and was a postdoctoral researcher at the University of Macau. His research spans wireless communications, cloud-edge collaboration, and IoT security. Dr. Dai has published in leading IEEE journals and has received multiple Best Paper Awards from international conferences.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 9819561590
• ISBN 13: 9789819561599
• Binding: Paperback
• Number of pages: 118