## 4 Evaluation
### Discussion
#### 4.3.3 Model Interpretability  
To ensure that the AI models integrated within our blockchain-assisted FL framework for digital twins in IIoT are not only secure and efficient but also transparent and interpretable, advanced model interpretability techniques were incorporated. Understanding the decision-making process of AI models is critical for building trust and enhancing usability in real-world industrial applications.  
To achieve this transparency, a continuous feedback loop using Local Interpretable Model-agnostic Explanations (LIME) was employed. LIME generated explanations for individual predictions made by the model, allowing stakeholders to assess the rationale behind each decision. The clarity and usefulness of these explanations were systematically evaluated, ensuring alignment with human intuition and domain knowledge. For instance, as shown in Figure 7, the model significantly relied on specific, high-impact image regions during prediction, providing evidence of its focus on relevant features.  
Building on this interpretability, the model became more adaptable and capable of real-time optimization by leveraging XAI-generated insights alongside raw data. These insights allowed the identification and prioritization of critical features, enhancing model accuracy and robustness. Moreover, the interpretability framework contributed to a self-optimizing digital twin system, designed specifically for dynamic IIoT environments.  
XAI further aids the training process of the global model by facilitating feature selection and prioritization. Techniques like LIME analyze feature importance across diverse datasets from edge devices, identifying impactful features to reduce dimensionality and improve training efficiency. XAI also supports error analysis, refining local training by focusing on misclassified data or features associated with higher prediction errors. Additionally, XAI-generated feature importance scores influence the federated averaging process by assigning greater weight to models trained on critical features, ensuring the aggregated global model captures essential insights. This feedback loop enables the global model to dynamically adapt to changes in data distribution, improving robustness, generalization, and overall performance.  
The resulting system is an interpretable, transparent, and effective FL model that not only strengthens operational effectiveness across industrial applications but also boosts  
Figure 7: Model’s performance contribution by each client node.  
confidence in predictions and judgments. This adaptability ensures that the model evolves continually, aligning with the dynamic processes and diverse contexts inherent in IIoT operations. The integration of interpretability techniques thus serves as a cornerstone for building trust and achieving actionable insights in industrial systems.  
XAI enhancements are summarized as follows:  
* Enabled fine-grained analysis of model outputs, improving trust among users.
* Provided actionable insights into feature importance, guiding model optimization efforts.
* Established a feedback loop for self-optimization by prioritizing data associated with high-impact features or errors.