This book conducts a comprehensive and detailed survey of the recent research efforts in edge intelligence. The authors first review the background and present motivation for AI running at the network edge. The book then provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. This second edition incorporates the latest research in this rapidly developing area. The authors also highlight the current applications and future research opportunities for edge intelligence.
Table of Contents:
Introduction to Edge Intelligence.- Edge Intelligence via Model Training.- Edge Intelligence via Federated Meta-Learning.- Resource-efficient Edge AI via Personalized Federated Learning.- Edge-Cloud Collaborative Learning via Distributionally Robust Optimization.- Hierarchical Mobile-Edge-Cloud Model Training with Hybrid Parallelism.- Communication-Efficient Hierarchical Federated Edge Learning.- Edge Intelligence via Model Inference.- On-Demand Accelerating Deep Neural Network Inference via Edge Computing.- Cooperative Edge DNN Inference with Adaptive Workload Partitioning.- Online Optimization and Resource Provisioning for Edge DNN Inference.- Applications, Marketplaces, and Future Directions of Edge Intelligence.
About the Author :
Sen Lin, Ph.D., is an Assistant Professor in the Department of Computer Science at the University of Houston. He received his Ph.D. degree from Arizona State University, M.S. from HKUST and B.E. from Zhejiang University. His research interests broadly fall in the intersection of machine learning and wireless networking. Currently, his research focuses on developing algorithms and theories in continual learning, meta-learning, reinforcement learning, adversarial machine learning and bilevel optimization, with applications in multiple domains, e.g., edge computing, security, network control.
Zhi Zhou, Ph.D., is an Associate Professor in the School of Computer Science and Engineering at Sun Yat-sen University. He earned his B.S., M.E., and Ph.D. degrees from Huazhong University of Science and Technology. His primary research interests encompass cloud computing, edge computing, and distributed systems.
Zhaofeng Zhang, Ph.D., isa Postdoctoral Researcher at School of Computing and Augmented Intelligence at Arizona State University. He received his B.Eng. degree in Electrical Engineering from Huazhong University of Science and Technology. He received his M.S. and Ph.D. degree in Electrical Engineering from Arizona State University. His research interests include edge computing, statistical machine learning, deep learning, and optimization.
Xu Chen, Ph.D., is a Full Professor and Assistant Dean at the School of Computer Science and Engineering at Sun Yat-sen University. He received his Ph.D. in Information Engineering from The Chinese University of Hong Kong. His research interests include edge computing, AI for networking, game theory, deep learning, and dynamic optimization.
Junshan Zhang, Ph.D. is a Professor in the Electrical and Computer Engineering Department at the University of California, Davis. He received his Ph.D. from the School of ECE at Purdue University. His research interests fall in the general field of information networks and data science, including edge intelligence, reinforcement learning, continual learning, network optimization and control, and game theory, with applications in connected and automated vehicles, 5G and beyond, wireless networks, IoT data privacy/security, and smart grid.
