Work

Digital Twins and Federated Learning for Industrial Internet of Things

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Science and Research Archive

DOI: 10.30574/ijsra.2025.16.1.2087

Authors: Md Hossain, Md Bahar Uddin

Institutions: California State University, Northridge, University of Dhaka, Bangladesh University of Textiles

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4412373126