## 2 Related Work
[31] combined edge learning with blockchain to improve predictive maintenance, while [PERSON] et al. [20] highlighted digital twins' potential in collaborative urban data analysis. [PERSON] et al. [32] applied federated modeling to smart grids, optimizing energy management in digital twin environments. [PERSON] et al. [19] and [PERSON] et al. [33] showcased blockchain-enabled digital twins' ability to deliver low-latency, secure data-driven insights. Despite these advances, scalability and interoperability in diverse IIoT settings remain significant gaps. XAI has gained prominence as a means of addressing the \"black-box\" nature of AI models in IIoT. [PERSON] et al. [34] discussed the challenges of AI interpretability in Industry 4.0, advocating for transparent and regulatory-compliant AI solutions. [PERSON] et al. [35] and [PERSON] et al. [36] emphasized the importance of integrating XAI into FL and blockchain networks to foster trust and transparency in industrial applications. However, standardized methodologies for deploying XAI in dynamic IIoT environments are lacking, and existing solutions often fail to balance computational efficiency with interpretability. As shown in Table 1, prior research primarily focuses on isolated applications of blockchain, FL, or digital twins. The proposed framework distinguishes itself by integrating these technologies with XAI, addressing the critical challenge of interpretability in high-stakes industrial environments. This technological synergy ensures that the framework not only enhances security and scalability but also provides transparency in its decision-making processes, a feature often overlooked in existing solutions. Moreover, the target applications of the reviewed works, as outlined in Table 1, are typically confined to specific domains, such as healthcare or smart cities. In contrast, the proposed framework demonstrates broader applicability, encompassing diverse IIoT scenarios. This versatility allows it to adapt to various industrial sectors, making it a more robust and comprehensive solution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Target Application** & **Technologies Employed** & **Key Contributions** \\ \hline [PERSON] et al. [17] & IIoT & FL, Blockchain & Developed resource-efficient FL with sharding, enhancing \\  & & & scalability and security for digital twin-driven IIoT. \\ \hline [PERSON] et al. [20] & Smart Cities & FL, Digital Twins & Highlighted FL’s scalability and privacy-preserving capabilities \\  & & & for collaborative urban planning. \\ \hline [PERSON] et al. [37] & IoT Data Management & Blockchain, Consensus & Introduced a consensus mechanism for secure data exchange in \\  & & Mechanisms & IoT environments, emphasizing scalability and reliability. \\ \hline [PERSON] et al. [38] & Healthcare (Cancer Care) & AI, Digital Twins & Leveraged digital twins and AI for predictive healthcare \\  & & & applications, focusing on patient-specific modeling. \\ \hline [PERSON] et al. [39] & IIoT & Blockchain, Digital Twins & Surveyed trends and challenges of integrating blockchain and \\  & & & digital twins in IIoT applications. \\ \hline [PERSON] et al. [30] & \begin{tabular}{l} Predictive Maintenance in \\ Construction \\ \end{tabular} & \
\begin{tabular}{l} Blockchain, AI, Digital \\ Twins \\ \end{tabular} &
\begin{tabular}{l} Proposed a framework combining blockchain and AI-driven \\ digital twins for predictive operations in the built environment. \\ \end{tabular} \\ \hline Our Framework & IIoT & Blockchain, FL, XAI & Integrates blockchain, FL, and XAI for secure, scalable, \\  & & and interpretable digital twin management in IIoT environments. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of related works.