Double Robust Federated Digital Twin Modeling in Smart Grid

Zhou, Yang; Ge, Yang; Jia, Li

doi:10.1109/jiot.2024.3450433

IEEE Internet Things J.

2024

DOI: 10.1109/jiot.2024.3450433

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Double Robust Federated Digital Twin Modeling in Smart Grid

Yang Zhou,

Yang Ge,

Li Jia

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2025

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A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT

Ababio,

Bieniek,

Rahouti

et al. 2025

Future Internet

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Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework’s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.

show abstract

A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT

Ababio,

Bieniek,

Rahouti

et al. 2025

Future Internet

View full text Add to dashboard Cite

show abstract

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Double Robust Federated Digital Twin Modeling in Smart Grid

Cited by 1 publication

References 58 publications

A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT

A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT

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