Mobile Edge Artificial Intelligence
Opportunities and Challenges
- 1st Edition - August 7, 2021
- Imprint: Academic Press
- Authors: Yuanming Shi, Kai Yang, Zhanpeng Yang, Yong Zhou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 1 7 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 3 5 - 6
Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient… Read more
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Request a sales quoteMobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains.
As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources.
- Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission
- Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface
- Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning
Scientists and researchers, postgraduates, undergraduates, practitioners and professionals in electronic engineering and computer science
- Cover image
- Title page
- Table of Contents
- Copyright
- List of figures
- Biography
- Yuanming Shi
- Kai Yang
- Zhanpeng Yang
- Yong Zhou
- Preface
- Acknowledgments
- Part One: Introduction and overview
- Chapter One: Motivations and organization
- Abstract
- 1.1. Motivations
- 1.2. Organization
- References
- Chapter Two: Primer on artificial intelligence
- Abstract
- 2.1. Basics of machine learning
- 2.2. Models of deep learning
- 2.3. Summary
- References
- Chapter Three: Convex optimization
- Abstract
- 3.1. First-order methods
- 3.2. Second-order methods
- 3.3. Summary
- References
- Chapter Four: Mobile edge AI
- Abstract
- 4.1. Overview
- 4.2. Edge inference
- 4.3. Edge training
- 4.4. Coded computing
- 4.5. Summary
- References
- Part Two: Edge inference
- Chapter Five: Model compression for on-device inference
- Abstract
- 5.1. Background on model compression
- 5.2. Layerwise network pruning
- 5.3. Nonconvex network pruning method with log-sum approximation
- 5.4. Simulation results
- 5.5. Summary
- References
- Chapter Six: Coded computing for on-device cooperative inference
- Abstract
- 6.1. Background on MapReduce
- 6.2. A communication-efficient data shuffling scheme
- 6.3. A low-rank optimization framework for communication-efficient data shuffling
- 6.4. Numerical algorithms
- 6.5. Simulation results
- 6.6. Summary
- References
- Chapter Seven: Computation offloading for edge cooperative inference
- Abstract
- 7.1. Background
- 7.2. Energy-efficient wireless cooperative transmission for edge inference
- 7.3. Computationally tractable approximation for probabilistic QoS constraints
- 7.4. Reweighted power minimization approach with DC regularization
- 7.5. Simulation results
- 7.6. Summary
- References
- Part Three: Edge training
- Chapter Eight: Over-the-air computation for federated learning
- Abstract
- 8.1. Background of federated learning and over-the-air computation
- 8.2. System model
- 8.3. Fast model aggregation via over-the-air computation
- 8.4. Sparse and low-rank optimization framework
- 8.5. Numerical algorithms
- 8.6. Simulation results
- 8.7. Summary
- References
- Chapter Nine: Reconfigurable intelligent surface aided federated learning
- Abstract
- 9.1. Background on reconfigurable intelligent surface
- 9.2. RIS empowered on-device distributed federated learning
- 9.3. Sparse and low-rank optimization framework
- 9.4. Simulation results
- 9.5. Summary
- References
- Chapter Ten: Blind over-the-air computation for federated learning
- Abstract
- 10.1. Blind over-the-air computation
- 10.2. Problem formulation
- 10.3. Wirtinger flow algorithm for blind over-the-air computation
- 10.4. Numerical results
- 10.5. Summary
- References
- Part Four: Final part: conclusions and future directions
- Chapter Eleven: Conclusions and future directions
- Abstract
- 11.1. Conclusions
- 11.2. Discussions and future directions
- Index
- Edition: 1
- Published: August 7, 2021
- Imprint: Academic Press
- No. of pages: 206
- Language: English
- Paperback ISBN: 9780128238172
- eBook ISBN: 9780128238356
YS
Yuanming Shi
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 currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. His research areas include optimization, machine learning, wireless communications, and their applications to 6G, IoT, and edge AI. He was a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society, and the 2021 IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He is also an editor of IEEE Transactions on Wireless Communications, IEEE Journal on Selected Areas in Communications, and Journal of Communications and Information Networks.
Affiliations and expertise
ShanghaiTech University, ChinaKY
Kai Yang
Mr. Kai Yang is currently at JD.com, Inc., China. His research interests include big data processing, mobile edge/fog computing, mobile edge artificial intelligence and dense communication networking. He has developed a wireless distributed computing framework for edge inference, and an over-the-air computation approach for edge federated machine learning.
Affiliations and expertise
Dalian University of Technology, ChinaZY
Zhanpeng Yang
Mr. Zhanpeng Yang is shortly to join the School of Information Science and Technology, ShanghaiTech University. He mainly focuses on developing reconfigurable intelligence surface based 6G wireless technologies for mobile edge AI systems.
Affiliations and expertise
Information Science and Technology, ShanghaiTech UniversityYZ
Yong Zhou
Yong Zhou received the B.Sc. and M.Eng. degrees from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the Ph.D. 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. He is currently an Assistant Professor in the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. He was the track co-chair of IEEE VTC 2020 Fall and 2023 Spring, as well as the general co-chair of IEEE ICC 2022 workshop on edge artificial intelligence for 6G. He co-authored the book Mobile Edge Artificial Intelligence: Opportunities and Challenges (Elsevier 2021). His research interests include 6G communications, edge intelligence, and Internet of Things.
Affiliations and expertise
Shandong University, Jinan, ChinaRead Mobile Edge Artificial Intelligence on ScienceDirect