Federated Learning for Industrial Internet of Things in Future Industries

The Industrial Internet of Things (IIoT) offers promising opportunities to revolutionize the operation of industrial systems and become a key enabler of 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 and confidential business information. In this article, we provide a detailed overview and discussions of the emerging applications of FL in several 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 future industries.

Introduction

Recent advances in communication and smart device technologies along with the rapid development of industrial informatization have promoted the proliferation of the Industrial Internet of Things (IIoT), with its capability to increase productivity and efficiency in industries [1]. It is anticipated that IIoT will play an increasingly significant role in the development of new applications, from smart manufacturing, smart factory to smart transportation and smart healthcare in the future industrial revolutions, from Industry 4.0 to Industry 5.0 and beyond. For example, IIoT can provide innovative solutions to drive smart manufacturing processes due to its ubiquitous sensing and computation capabilities.