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 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.

在《未來智能無線網絡的聯邦學習》中，一個由傑出研究人員組成的團隊提供了一個堅實而深入的資源集合，涵蓋了支持聯邦學習的基礎概念和算法，以及它們如何應用於無線通信系統。編輯們收錄了一些研究作品，探討了通信資源配置如何影響聯邦學習的性能、準確性、收斂性、可擴展性以及安全和隱私性。

在這本書中，讀者將探索一系列主題，展示了聯邦學習算法、概念以及適用於無線通信的設計和優化問題。讀者還將找到：

- 對聯邦學習的基本概念和算法（包括水平、垂直和混合型聯邦學習）的全面介紹
- 對於聯邦學習的無線通信網絡設計和優化的深入探討
- 對未來無線網絡的新型聯邦學習算法和框架的實用討論
- 在邊緣智能、自動駕駛、物聯網、多邊緣計算、區塊鏈以及內容緩存和分發等方面的廣泛案例研究

這本書非常適合電氣和計算機科學工程師、研究人員、教授和研究生，對機器學習感興趣的讀者。同時，監管機構和負責制定人工智能政策的機構也能從中受益。

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 in the United Kingdom. He was formerly a Research Fellow at the 5G Innovation Centre at the University of Surrey.

Yao Sun博士是英國格拉斯哥大學的講師。他曾是中國成都電子科技大學的研究員。Chaoqun You是新加坡科技與設計大學的研究員。她曾是多倫多大學電子計算機工程系的學術訪問學者。Gang Feng是中國電子科技大學的教授。他曾是南洋理工大學的副教授。Lei Zhang博士是英國格拉斯哥大學的教授。他曾是薩里大學5G創新中心的研究員。