Federated Edge Learning: Algorithms, Architectures and Trustworthiness
暫譯: 聯邦邊緣學習：演算法、架構與可信性
Zhou, Yong, Fang, Wenzhi, Shi, Yuanming
- 出版商: Springer
- 出版日期: 2025-08-30
- 售價: $7,230
- 貴賓價: 9.5 折 $6,869
- 語言: 英文
- 頁數: 190
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031966481
- ISBN-13: 9783031966484
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相關分類:
Edge computing
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book presents various effective schemes from the perspectives of algorithms, architectures, privacy, and security to enable scalable and trustworthy Federated Edge Learning (FEEL). From the algorithmic perspective, the authors elaborate various federated optimization algorithms, including zeroth-order, first-order, and second-order methods. There is a specific emphasis on presenting provable convergence analysis to illustrate the impact of learning and wireless communication parameters. The convergence rate, computation complexity and communication overhead of the federated zeroth/first/second-order algorithms over wireless networks are elaborated.
From the networking architecture perspective, the authors illustrate how the critical challenges of FEEL can be addressed by exploiting different architectures and designing effective communication schemes. Specifically, the communication straggler issue of FEEL can be mitigated by utilizing reconfigurable intelligent surface and unmanned aerial vehicle to reconfigure the propagation environment, while over-the-air computation is utilized to support ultra-fast model aggregation for FEEL by exploiting the waveform superposition property. Additionally, the multi-cell architecture presents a feasible solution for collaborative FEEL training among multiple cells. Finally, the authors discuss the challenges of FEEL from the privacy and security perspective, followed by presenting effective communication schemes that can achieve differentially private model aggregation and Byzantine-resilient model aggregation to achieve trustworthy FEEL.
This book is designed for researchers and professionals whose focus is wireless communications. Advanced-level students majoring in computer science and electrical engineering will also find this book useful as a reference.
商品描述(中文翻譯)
本書從演算法、架構、隱私和安全的角度介紹了各種有效的方案，以實現可擴展且值得信賴的聯邦邊緣學習（Federated Edge Learning, FEEL）。從演算法的角度，作者詳細闡述了各種聯邦優化演算法，包括零階、一階和二階方法。特別強調提供可證明的收斂分析，以說明學習和無線通信參數的影響。書中詳細說明了聯邦零階/一階/二階演算法在無線網絡上的收斂速率、計算複雜度和通信開銷。
從網絡架構的角度，作者說明了如何通過利用不同的架構和設計有效的通信方案來解決FEEL的關鍵挑戰。具體而言，FEEL的通信延遲問題可以通過利用可重構智能表面和無人機來重新配置傳播環境來減輕，而通過利用波形疊加特性，無線計算被用來支持超快速的模型聚合。此外，多小區架構為多小區之間的協作FEEL訓練提供了一個可行的解決方案。最後，作者從隱私和安全的角度討論了FEEL的挑戰，並提出了能夠實現差分隱私模型聚合和拜占庭容錯模型聚合的有效通信方案，以實現值得信賴的FEEL。
本書旨在為專注於無線通信的研究人員和專業人士提供參考。主修計算機科學和電機工程的高級學生也會發現本書對他們有用。
作者簡介
Yong Zhou received the BSc and MEng degrees from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the PhD degree from the University of Waterloo, Waterloo, ON, Canada, in 2015. From Nov 2015 to Jan 2018, he worked as a postdoctoral research fellow in the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada. Since Mar. 2018, he has been with the School of Information Science and Technology, ShanghaiTech University, Shanghai, China, where he is currently a Tenured Associate Professor. His research interests include 6G communications, edge intelligence, and Internet of Things.
Wenzhi Fang received his B.S. degree from Shanghai University in 2020 and completed his master's degree at ShanghaiTech University in 2023. His research interests focus on optimization theory and its application in machine learning.
Yuanming Shi received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is a Full Professor. His research areas include edge artificial intelligence and large-scale optimization. He is a recipient of the IEEE Marconi Prize Paper Award in Wireless Communications in 2016, the Young Author Best Paper Award by the IEEE Signal Processing Society in 2016, the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2021, the Chinese Institute of Electronics First Prize in Natural Science in 2022, and the China Institute of Communications First Prize in Natural Science in 2024. He is an IET Fellow.
Khaled B. Letaief is a globally recognized leader in wireless communications and networks, with a research focus that spans artificial intelligence, integrated sensing and communication, mobile cloud and edge computing, federated learning, and 6G systems. Dr. Letaief is a distinguished member of several esteemed organizations, including the United States National Academy of Engineering, IEEE Fellow, and Fellow of the Hong Kong Institution of Engineers. He is also a member of the Hong Kong Academy of Engineering. His accolades include numerous prestigious awards, such as the 2024 IEEE James Evans Avant Garde Award, 2024 Distinguished Purdue University Alumni Award, 2022 IEEE Edwin Howard Armstrong Achievement Award, and 2021 IEEE Communications Society Best Survey Paper Award. He has also received the 2019 Joint Paper Award from the IEEE Communications Society and Information Theory Society, the 2016 IEEE Marconi Prize Award in Wireless Communications, and over 20 IEEE Best Paper Awards.
Since 1993, Dr. Letaief has been a faculty member at The Hong Kong University of Science and Technology (HKUST), where he has held multiple leadership roles, including Senior Advisor to the President, Acting Provost, Head of the Electronic and Computer Engineering Department, Director of the Wireless IC Design Center, and Director of the Hong Kong Telecom Institute of Information Technology. He served as Chair Professor and Dean of Engineering at HKUST and, from 2015 to 2018, was Provost at Hamad Bin Khalifa University in Qatar, where he played a key role in establishing a research-intensive university in collaboration with renowned institutions like Northwestern University, Carnegie Mellon University, Cornell, and Texas A&M. He earned his B.S. degree with distinction in Electrical Engineering from Purdue University in December 1984, followed by an M.S. and Ph.D. in Electrical Engineering from the same institution in August 1986 and May 1990, respectively. In 2022, he received an honorary Ph.D. from the University of Johannesburg, South Africa.
作者簡介(中文翻譯)
Yong Zhou於2008年和2011年分別在中國濟南的山東大學獲得學士和碩士學位，並於2015年在加拿大安大略省滑鐵盧大學獲得博士學位。從2015年11月到2018年1月，他在加拿大溫哥華的英屬哥倫比亞大學電機與計算機工程系擔任博士後研究員。自2018年3月以來，他一直在中國上海的上海科技大學資訊科學與技術學院任教，目前擔任終身副教授。他的研究興趣包括6G通訊、邊緣智能和物聯網。
Wenzhi Fang於2020年在上海大學獲得學士學位，並於2023年在上海科技大學完成碩士學位。他的研究興趣集中在優化理論及其在機器學習中的應用。
Yuanming Shi於2011年在中國北京的清華大學獲得電子工程學士學位，並於2015年在香港科技大學獲得電子與計算機工程博士學位。自2015年9月以來，他一直在上海科技大學資訊科學與技術學院任教，現為正教授。他的研究領域包括邊緣人工智能和大規模優化。他曾獲得2016年IEEE無線通訊馬可尼獎論文獎、2016年IEEE信號處理學會最佳青年作者論文獎、2021年IEEE通訊學會亞太區傑出青年研究者獎、2022年中國電子學會自然科學一等獎，以及2024年中國通信學會自然科學一等獎。他是IET的院士。
Khaled B. Letaief是全球公認的無線通訊和網絡領域的領導者，研究重點涵蓋人工智能、集成感知與通訊、移動雲端與邊緣計算、聯邦學習和6G系統。Letaief博士是多個知名組織的傑出成員，包括美國國家工程院、IEEE Fellow和香港工程師學會的院士。他也是香港工程學院的成員。他的榮譽包括多個著名獎項，如2024年IEEE James Evans Avant Garde獎、2024年普渡大學傑出校友獎、2022年IEEE Edwin Howard Armstrong成就獎和2021年IEEE通訊學會最佳調查論文獎。他還獲得了2019年IEEE通訊學會和信息理論學會的聯合論文獎、2016年IEEE無線通訊馬可尼獎，以及超過20項IEEE最佳論文獎。
自1993年以來，Letaief博士一直是香港科技大學的教職員，並擔任多個領導職位，包括校長高級顧問、代理教務長、電子與計算機工程系主任、無線IC設計中心主任和香港電信資訊技術研究所所長。他曾擔任香港科技大學的講座教授和工程學院院長，並於2015年至2018年擔任卡塔爾哈馬德·賓·哈利法大學的教務長，在那裡他在與西北大學、卡內基梅隆大學、康奈爾大學和德克薩斯A&M大學等知名機構合作建立一所研究密集型大學方面發揮了關鍵作用。他於1984年12月在普渡大學以優異成績獲得電機工程學士學位，並於1986年8月和1990年5月在同一機構獲得電機工程碩士和博士學位。2022年，他獲得南非約翰尼斯堡大學的榮譽博士學位。
