Towards an Intelligent Edge: Wireless Communication Meets Machine Learning

Abstract

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (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 interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, 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 (MEC) 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 principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified.

submitted to IEEE for possible publication

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Cited by in corpus (16)

• Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
• A Crowdsourcing Framework for On-Device Federated Learning
• 6G White Paper on Edge Intelligence
• High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
• Delay Minimization for Federated Learning Over Wireless Communication Networks
• Federated Learning via Over-the-Air Computation
• Convergence Time Optimization for Federated Learning over Wireless Networks
• Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
• Wireless for Machine Learning
• Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
• Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing
• An Introduction to Communication Efficient Edge Machine Learning
• Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
• Wireless Networked Control Systems with Coding-Free Data Transmission for Industrial IoT
• SecEL: Privacy-Preserving, Verifiable and Fault-Tolerant Edge Learning for Autonomous Vehicles
• Quantized deep learning models on low-power edge devices for robotic systems