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Herausgeber: 
  • Zhang Lei
  • Yao Sun
  • Chaoqun You
  • Feng Gang
  • Federated Learning for Future Intelligent Wireless Networks 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2023  
    Genre:  Naturwissensch., Medizin, Technik 
     
    AI / Artificial Intelligence / Communication Technology - Networks / computer science / drahtlose kommunikation / Electrical & Electronics Engineering / Elektrotechnik u. Elektronik / Informatik
    ISBN:  9781119913894 
    EAN-Code: 
    9781119913894 
    Verlag:  Wiley 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 229 mm / B 152 mm / D 20 mm 
    Gewicht:  694 gr 
    Seiten:  320 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers delivers a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. In the book, readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues that apply to wireless communications. Readers will also find: * A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL * Comprehensive explorations of wireless communication network design and optimization for federated learning * Practical discussions of novel federated learning algorithms and frameworks for future wireless networks * Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.

      



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