Federated Learning for Smart Mobility
Towards Secure, Efficient, and Sustainable Transportation System
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Beschreibung
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.
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.
Bridges federated learning theory with real-world smart mobility and IoT applications Highlights privacy, security, and efficiency challenges in next-generation transportation systems Offers concise, state-of-the-art insights for researchers, engineers, and graduate students
Autor*in
Jiaming Pei
Themen in »Federated Learning for Smart Mobility«
Federated Learning Smart Mobility Intelligent Transportation Systems (ITS) Internet of Things (IoT) Vehicle-to-Everything (V2X) Privacy-Preserving Machine Learning Edge Computing Cloud-Edge Collaboration Data Security and Trustworthy AI Traffic Flow Prediction Autonomous Driving Cooperative Perception IoT Data Heterogeneity Sustainable Transportation 6G and Future Internet