1 Bangladesh University of Textiles (Former Name: College of Textile Technology, under the University of Dhaka), Dhaka - 1208, Bangladesh.
2 Department. of Manufacturing Systems Engineering and Management, California State University, Northridge, CA 91330, USA.
3 Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA.
4 Department of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
International Journal of Science and Research Archive, 2025, 16(01), 729-736
Article DOI: 10.30574/ijsra.2025.16.1.2087
Received on 01 June 2025; revised on 08 July 2025; accepted on 10 July 2025
The Internet of Things (IoT) is penetrating various facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications by allowing for AI training at distributed IoT devices without the need for data sharing. In this survey, we focus on DT and FL for IIoT. Initially, we analyzed the existing surveys. In this paper, we present the applications of DT and FL in IIoT.
Digital Twins; Federated Learning; Industry 4.0; Cyber–Physical System; Industrial Internet of Things (IIoT)
Preview Article PDF
Md Hossain and Md Bahar Uddin. Digital Twins and Federated Learning for Industrial Internet of Things. International Journal of Science and Research Archive, 2025, 16(01), 729-736. Article DOI: https://doi.org/10.30574/ijsra.2025.16.1.2087.
