## 4 Evaluation
### Methodology  
In the present investigation, a comparative analysis of centralized and federated learning methodologies was performed using a PPI prediction model predicted on XGBoost (v.2.0.3). Experimental trials were executed in identical environments for both methodologies, consistently employing the same hyperparameters. Hyperparameters were applied by referring to a model [35] that showed excellent performance in existing centralized learning, and the settings showed optimal performance in an environment similar to the characteristics of the data in this study. Based on this, in order to ensure fairness in comparing results in this study, the settings of centralized learning and federated learning were set to be the same so that the performance difference could be objectively evaluated and analyzed. Figure 4 demonstrates the hyperparameters for the training model. Each hospital's dataset employed the SimpleImputer technique to substitute absent values with the mean of each column, preserving data continuity and minimizing the risk of data loss. Furthermore, owing to discrepancies in feature arrangement across datasets, a systematic approach to harmonize feature order was implemented in both centralized and federated learning paradigms. The evaluation of performance was predicated on the metrics of accuracy and ROC-AUC, which were chosen as the evaluation methods because they are particularly well-suited for assessing both classification performance and model prediction effectiveness, especially in the context of imbalanced datasets. For centralized learning, an XGBoost PPI prediction model was constructed utilizing each \begin{table}
\begin{tabular}{c c c} \hline \hline  & PPI Negative & PPI Positive \\ \cline{2-3}  & n = 207 & n = 52 \\ \hline DeltaQRS & 14.01 \(\pm\) 23.29 & 27.25 \(\pm\) 27.97 \\ \hline New\_Onset\_LBBB & 1(43)/0(164) & 1(15)/0(37) \\ \hline ASA & 1(150)/0(57) & 1(30)/0(22) \\ \hline AntiPlateletotherthanASA & 1(73)/0(134) & 1(26)/0(26) \\ \hline AntiPlateTherapy & 1(169)/0(38) & 1(42)/0(10) \\ \hline Beta\_Blocker & 1(130)/0(77) & 1(33)/0(19) \\ \hline CCB & 1(47)/0(160) & 1(16)/0(36) \\ \hline ACEi\_ARB & 1(93)/0(114) & 1(24)/0(28) \\ \hline Diuretics & 1(118)/0(89) & 1(32)/0(20) \\ \hline Aldosteroneantagonist & 1(28)/0(179) & 1(5)/0(47) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 4: Model training hyperparameters. dataset. Following the application of uniform hyperparameter configurations, the efficacy of the model was assessed using corresponding test datasets. In centralized learning, an XGBoost PPI prediction model was established for each dataset, with all models across the three datasets employing identical hyperparameters, as evidenced in the documentation. After the training phase, the developed models were rigorously evaluated for accuracy and ROC-AUC metrics using the respective test datasets. For federated learning, three clients associated with each of the three datasets were engaged, with all the clients actively participating in every learning iteration. The cumulative number of learning rounds was 30, during which all clients contributed to the evaluation process. The aggregation method employed for the global model was [PERSON]'s FedXGBBagging, which entails the independent training of each client's model, followed by their integration using a bagging approach. The XGBoost model was implemented in a manner analogous to that employed in centralized learning with the hyperparameters maintained consistently as delineated in the referenced documentation. Each client is responsible for training a model using local data and subsequently transmitting the data to a central server, which aggregates the contributions to formulate a global model in accordance with the FedXGBBagging methodology. After the federated learning process, the resultant global model was utilized to evaluate the accuracy and ROC-AUC performance based on the test datasets from each client.