## 7 Results  
Firstly, we present the performance results of the identity management system that demonstrate that the system maintains low latency across all key operations, indicating strong suitability for real-world deployment. The initial phase of establishing a local server for the virtual Identity Provider (vIdP) entails integrating multiple partial IdP servers, which collectively require approximately 20 min to configure. These servers work together to securely manage user identities by establishing the essential keys needed for the identity management process. Likewise, setting up a web-facing service provider and client application for AURORA MINDS demands 9.35 min to finalize. This process includes a testing phase to verify the effectiveness of all installed security parameters. Once the Administrator logs into the system and selects the appropriate role for each user, the users will receive an OTP to complete the registration process. Then, the users log into the client app, enter the OTP, and are redirected to the Verification System page. Upon successful registration, a confirmation message is received within 0.22 s.  
Next, after the users have received their credentials, they can log into the AURORA MINDS client app and perform the following actions. By utilizing the saved credentials in local storage, they can quickly access/log in to the user client app within 0.30 s. After successful registration, they can securely store their credentials through the AURORA MINDS dashboard in 13 s.  
Furthermore, when a user wishes to verify their possessed credentials, they can utilize the credential storage to select the attributes they want to reveal through p-ABC technology. The user also provides their consent and receives a message confirming that the verification process is completed within 0.39 s. The execution times of identity management operations are presented in Table 5 below. Secondly, we present the results of the machine learning models under three different settings, i.e., individual learning, in which clients perform only local training without \begin{table}
\begin{tabular}{l c} \hline \hline  & **Operation** & **Time (s)** \\ \hline Registration & 0.22 \\ Login & 0.30 \\ Credential Issuance & 0.23 \\ Credential Storage & 13.00 \\ Verification & 0.39 \\ Presentation & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution times of identity management operations. collaboration; centralized learning, in which all the data is used to train a common global model; and Federated Learning, during which each client trains its model, but they also collaborate by aggregating their model parameters to create a global model with better performance. The results of Table 6 illustrate the trade-offs and advantages of each learning paradigm. Individual learning excels in local discrimination (AUC-ROC: 0.8) but lacks robustness and generalization due to isolated training, as reflected in its low SIREOS (0.05) and moderate AP (0.74). Centralized learning leverages a unified dataset to achieve a high robustness score (SIREOS: 0.0513) and balanced precision-recall performance (AP: 0.7498), but it comes at the expense of data privacy and potential overfitting to heterogeneous data. Federated Learning stands out by striking a balance between privacy, performance, and generalization. Achieving an AUC-ROC (0.7925) comparable to centralized learning, it preserves data locality, ensuring compliance with privacy regulations such as GDPR. While its robustness (SIREOS: 0.050) slightly trails centralized learning, Federated Learning provides sufficient stability across clients and enhances the generalization capacity of the global model. This makes it a viable and scalable approach for privacy-sensitive applications, such as healthcare, where the trade-off between privacy and model performance is paramount. These findings confirm Federated Learning's potential to enable collaborative intelligence without sacrificing individual data privacy, making it an innovative solution in secure machine learning systems.