Federated learning smart mobility par pei jiaming (7 résultats)

Paperback or Softback. Etat : New. Federated Learning for Smart Mobility: Towards Secure, Efficient, and Sustainable Transportation System. Book.

Etat : New.

Taschenbuch. Etat : Neu. Federated Learning for Smart Mobility | Towards Secure, Efficient, and Sustainable Transportation System | Jiaming Pei (u. a.) | Taschenbuch | xiv | Englisch | 2026 | Springer | EAN 9789819561599 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch.…

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Paperback. Etat : new. Paperback. 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 FLs 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 challengesincluding data heterogeneity, system scalability, and privacy protectionand 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 120 pp. Englisch.…