With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviewsthe background and present motivation for AI running at the network edge. Next, it 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. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

Autorentext

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.

Inhalt
Preface.- Acknowledgments.- Introduction to Edge Intelligence.- Edge Intelligence via Model Training.- Edge-Cloud Collaborative Learning via Distributionally Robust Optimization.- Hierarchical Mobile-Edge-Cloud Model Training with Hybrid Parallelism.- Edge Intelligence via Model Inference.- On-Demand Accelerating Deep Neural Network Inference via Edge Computing.- Applications, Marketplaces, and Future Directions of Edge Intelligence.- Bibliography.- Authors' Biographies.