论文标题
返回青睐：无线网络可以为AI和Edge学习提供什么
Returning the Favor: What Wireless Networking Can Offer to AI and Edge Learning
论文作者
论文摘要
机器学习（ML）和人工智能（AI）最近对改善无线网络的运营并在边缘建立情报方面产生了重大影响。作为回报，艰难的努力是为了探索如何适应，优化和安排无线网络如何有助于在边缘实施ML/AI。本文旨在通过对无线网络研究人员如何利用他们的专业知识来将青睐回到边缘学习的愿景来解决这一空白。它将审查启用技术，总结此路径上的首届作品，并阐明不同的方向，以建立移动边缘学习（MEL）的全面框架。
Machine learning (ML) and artificial intelligence (AI) have recently made a significant impact on improving the operations of wireless networks and establishing intelligence at the edge. In return, rare efforts were made to explore how adapting, optimizing, and arranging wireless networks can contribute to implementing ML/AI at the edge. This article aims to address this void by setting a vision on how wireless networking researchers can leverage their expertise to return the favor to edge learning. It will review the enabling technologies, summarize the inaugural works on this path, and shed light on different directions to establish a comprehensive framework for mobile edge learning (MEL).
