Secure and decentralized digital twin optimization: A blockchain-enabled federated learning approach for IIoT

Enhancing the functionality of digital twins within the Industrial Internet of Things (IIoT) demands robust and flexible AI-driven solutions. Digital twins, which serve as virtual counterparts to physical assets, have emerged as a critical innovation for facilitating real-Time operational insights. However, their effective deployment is hindered by key challenges, including safeguarding data integrity, handling extensive data flows, and preserving privacy. This study introduces a novel approach that integrates blockchain technology with federated learning (FL) to overcome these obstacles. By enabling AI models to operate on edge devices and employing FL, data remains localized, minimizing privacy risks while fostering secure collaboration among industrial components. The incorporation of blockchain enhances data governance, enforces access controls, and ensures transparency, thereby enabling interpretable and trustworthy AI-driven decision-making. The proposed system aims to improve the security, privacy, scalability, and explainability of self-optimizing digital twins in IIoT ecosystems. Additionally, real-world testing is conducted to evaluate the framework's capacity to strengthen security, transparency, and autonomous optimization, positioning it as a transformative solution for increasing industrial efficiency and resilience.