[论文解读] Towards an Intelligent Edge: Wireless Communication Meets Machine Learning

Guangxu Zhu, Dongzhu Liu+4 more|arXiv (Cornell University)|Sep 2, 2018

IoT and Edge/Fog Computing参考文献 8被引用 56

一句话总结

本文主张在边缘学习中采用学习驱动的通信，将无线设计与移动/MEC 平台上的分布式机器学习结合，以在边缘实现 AI。

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.

研究动机与目标

• 激励在网络边缘运行 AI 驱动应用的边缘学习范式。
• 识别边缘设备计算能力和数据有限所带来的挑战，以及需要利用 MEC 与分布式数据。
• 提出一套新的无线通信设计原则以支持边缘学习，称为学习驱动的通信。
• 强调分布式学习与通信的融合是一个耦合且以优化驱动的问题。

提出的方法

• 倡导一个将无线通信与边缘学习整合的新设计框架。
• 给出实例来展示学习驱动的通信原则的有效性。
• 讨论无线网络与边缘设备上的机器学习在交叉点的机遇与挑战。

实验结果

研究问题

• RQ1 如何重新设计无线通信以更好地支持在 MEC 平台上的分布式边缘学习？
• RQ2 在资源受限的条件下，从分布式边缘设备的数据学习的主要挑战是什么？
• RQ3 在智能边缘环境中将通信设计与机器学习融合会带来哪些机遇？

主要发现

• 示例展示了学习驱动的通信在边缘学习场景中的潜在有效性。
• 确定了无线通信与边缘智能中的机器学习交叉领域的新研究机会。
• 强调在分布式边缘系统中需联合优化数据共享、计算和通信。