Federated Learning for Future Intelligent Wireless Networks
Description
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.
<b>Explore the concepts, algorithms, and applications underlying federated learning</b>
In <i>Federated Learning for Future Intelligent Wireless Networks</i>, 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:
<ul><li>A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL</li><li>Comprehensive explorations of wireless communication network design and optimization for federated learning</li><li>Practical discussions of novel federated learning algorithms and frameworks for future wireless networks</li><li>Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution</li></ul>Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, <i>Federated Learning for Future Intelligent Wireless Networks</i> will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.
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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 in the United Kingdom. He was formerly a Research Fellow at the 5G Innovation Centre at the University of Surrey.
<b>Yao Sun, PhD, </b>is a Lecturer with the University of Glasgow in the United Kingdom. He was a former Research Fellow at UESTC in Chengdu, China.
<b>Chaoqun You </b>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.
<b>Gang Feng</b> is a Professor at the University of Electronic Science and Technology of China. He was an associate professor at Nanyang Technological University.
<b>Lei Zhang, PhD, </b>is a Professor at the University of Glasgow in the United Kingdom. He was formerly a Research Fellow at the 5G Innovation Centre at the University of Surrey.