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Federated Learning for Future Intelligent Wireless Networks

€122.99
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6g
6g federated learning
6g fl
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B01=Chaoqun You
B01=Gang Feng
B01=Lei Zhang
B01=Yao Sun
Category1=Non-Fiction
Category=TJKW
Category=UYQM
COP=United States
Delivery_Delivery within 10-20 working days
edge intelligence
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_tech-engineering
federated learning algorithms
federated learning applications
federated learning case studies
federated learning challenges
Federated learning operations
federated learning privacy
fl
Language_English
PA=Not available (reason unspecified)
Price_€100 and above
PS=Active
softlaunch

Product details

  • ISBN 9781119913894
  • Weight: 694g
  • Publication Date: 28 Nov 2023
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description
Federated Learning for Future Intelligent Wireless Networks

Explore the concepts, algorithms, and applications underlying federated learning

In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver 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.

Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues 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.
About

Yao Sun, PhD, is a Lecturer with the University of Glasgow in the United Kingdom. He was a former Research Fellow at UESTC in Chengdu, China.

Chaoqun You is a Research Fellow at the Singapore University of Technology and Design. She was formerly an Academic Guest with the Department of Electronic Computer Engineering at the University of Toronto.

Gang Feng is a Professor at the University of Electronic Science and Technology of China. He was an Associate Professor at Nanyang Technological University.

Lei Zhang, PhD, is a Professor at the University of Glasgow, UK. He was formerly a Research Fellow at the 5G Innovation Centre at the University of Surrey.