The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy. In this article, we provide a detailed overview and discussions of the emerging applications of FL in key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in industries.
翻译:最近,人工智能(AI)被广泛用于实现智能的IIOT应用,因为AI技术需要集中收集和处理数据;然而,由于现代IIOT网络的高度可扩展性和工业数据保密性不断提高,在现实情况下,这并不总是可行; 联邦学习(FL)作为一种新兴的AI协作方法,通过协调多种IIOT装置和机器在网络边缘进行AI培训,同时帮助保护用户隐私,对智能的IIOT网络特别具有吸引力; 在本条中,我们详细概述和讨论关键IIOT服务和应用中FL的新兴应用; 提供案例研究,以展示IIOT中FL的可行性。 最后,我们强调工业全面实现FL-IIOT需要解决的一系列开放性研究课题。