Federated Learning for Smart Mobility
Springer Verlag, Singapore
978-981-95-6159-9 (ISBN)
- Titel nicht im Sortiment
- Artikel merken
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.
Introduction to Federated Learning for Smart Mobility.- Foundations of FL and IoT Systems.- FL Applications in Smart Mobility.- Case Studies and Research Insights.- Challenges and Future Directions.
| Erscheint lt. Verlag | 6.2.2026 |
|---|---|
| Reihe/Serie | SpringerBriefs in Computer Science |
| Zusatzinfo | 25 Illustrations, black and white |
| Verlagsort | Singapore |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | 6G and Future Internet • Autonomous Driving • Blockchain for Federated Learning • cloud-edge collaboration • Cooperative Perception • Data Security and Trustworthy AI • decentralized ai • edge computing • federated learning • Intelligent Transportation Systems (ITS) • Internet of Things (IoT) • IoT Data Heterogeneity • Privacy-Preserving Machine Learning • Smart City Applications • smart mobility • Sustainable transportation • Traffic Flow Prediction • vehicle-to-everything (V2X) |
| ISBN-10 | 981-95-6159-0 / 9819561590 |
| ISBN-13 | 978-981-95-6159-9 / 9789819561599 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
