The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified. Jun ZhanG [M'10, SM'15] is an assistant professor in the Department of EIE at Hong Kong Polytechnic University. His research interests include 5G systems, edge computing/learning, and big data analytics. Kaibin huanG [M'08, SM'13] is an associate professor with the Department of EEE, University of Hong Kong. His research interests include mobile edge computing, distributed learning, and 5G systems.
In REF , the authors provided new design principles to characterize edge-learning and highlighted important research opportunities and applications with the new philosophy for wireless communication called learning-driven communication.
Toward an Intelligent Edge: Wireless Communication Meets Machine Learning
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