The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Therefore, AI is envisioned to become native and ubiquitous in future communication and networking systems. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to the state-of-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify the potential future opportunities.
翻译:云本解决方案正在变得效率低下,原因是大量时间延误、高电耗、高安全和隐私问题,其原因是数十亿个接线无线装置以及通常在网络边缘产生的数据相位数造成的数十亿个无线装置和它们通常产生的数据相位数造成的高电耗、安全和隐私问题。边缘计算和人工智能(AI)技术的混合可以最佳地将机智计算服务器更接近网络边缘,为先进的无线应用软件(例如视频/视听监视和个人建议系统)提供支持,在需要时在数据生成点进行计算时进行智能决策,并分发机器学习(ML),有可能避免传播大型数据集,避免云本中央学习中可能存在的隐私折叠式折叠式。因此,AI预计将成为未来通信和网络的本地化和无处式智能(AI)技术的最佳组合。本文全面概述了在本地-AI无线网络保护伞下无线网络中传播情报的最新进展,重点是本地-AI无线网络的基本概念、AI辅助边缘计算、设计可兼容通信网络的分布式学习架构、通信节能技术的传播机会,以及我们向最新数据库中传播的升级结构中传播的现有学习。