Yang, Wei, Yang, Yuan, Xiang, Wei, Yuan, Lei, Yu, Kan, Alonso, Álvaro Hernández, Ureña, Jesús Ureña, and Pang, Zhibo (2024) Adaptive optimization federated learning enabled digital twins in industrial IoT. Journal of Industrial Information Integration, 41. 100645.

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Abstract

The Industrial Internet of Things (IIoT) plays a pivotal role in steering enterprises towards comprehensive digital transformation and fostering intelligent production, which serves as a critical pillar of Industry 4.0. Digital twin (DT) emerges as a highly promising technology, enabling the digital transformation of the IIoT by seamlessly bridging physical systems with digital spaces. However, the overall service quality of the IIoT is severely impacted by the resource-limited devices and the massive, heterogeneous and sensitive data in the IIoT. As an innovative distributed machine learning paradigm, federated learning (FL) inherently possesses advantages in handling private and heterogeneous data. In this paper, we propose a novel framework integrating FL with DT-enabled IIoT, termed FDEI, which combines the merits of both to improve service quality while maintaining trustworthiness. To enhance the modeling efficiency, we develop FedOA, an adaptive optimization FL method that dynamically adjusts the local update coefficient and model compression rate in resource-limited IIoT scenarios, to construct the FDEI model. Specifically, leveraging the interdependence between the two variables, we conduct a theoretical analysis of the model convergence rate and derive the associated convergence bounds. Building upon the theoretical analysis, we further propose a joint adaptive adjustment strategy by optimizing the two variables across various clients to minimize runtime differences and accelerate the convergence rate. Numerical results demonstrate that our proposed approach achieves an approximate 68% improvement in convergence speed and a reduction of approximately 66% in traffic consumption compared to the benchmarks (e.g., FedAvg, AFL, and CSFL).

Item ID:	85484
Item Type:	Article (Research - C1)
ISSN:	2452-414X
Copyright Information:	© 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Date Deposited:	14 May 2025 22:56
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460603 Cyberphysical systems and internet of things @ 70% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 30%
SEO Codes:	24 MANUFACTURING > 2499 Other manufacturing > 249999 Other manufacturing not elsewhere classified @ 50% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50%
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