Federated Learning for Industrial Internet of Things in Future Industries

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)192-199
Number of pages8
JournalIEEE Wireless Communications
Volume28
Issue number6
DOIs
StatePublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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