## 5 Results
### Comparison of CL and FL  
A performance comparison between CL and FL was conducted using the evaluation data presented in Table 3. As seen in Figure 2, in the CL environment, the test set was used, while in FL, a dataset was employed specifically for evaluating the global model. The performance comparison between CL and FL is shown in Table 4. The accuracy-based LMECS showed slightly higher accuracy (81.56 \(\pm\) 1.63) than CL (81.16 \(\pm\) 0.29) and outperformed other existing FL algorithms (Figure 3). In sensitivity, which is an indicator of how well the intensive care unit was identified, accuracy-based LMECS (83.81 \(\pm\) 0.33) showed the most similar performance to CL (84.02 \(\pm\) 0.16) and was superior to the existing FL algorithm (Figure 4). Specificity was highest in FedAvg (83.23 \(\pm\) 2.58) compared to CL,LMECS, and FedProx. However, the lower sensitivity of FedAvg (79.13 \(\pm\) 1.73) suggests that the model did not adequately capture the nuances of critical care and tended to train in the direction of typically non-critical care. FedAvg's selection of clients was random, leading to a model trained with clients having less critical care information (severe non-IID data). The PPV and NPV metrics indicated that LMECS was almost similar to CL and superior to other existing FL algorithms. The low performance of PPV and high performance of NPV can be attributed to the relatively low proportion of critical care cases in the total dataset, as shown in Table 3. In terms of AUC, which considers both the sensitivity and specificity for critical care, accuracy-based LMECS (81.33 \(\pm\) 0.21) was competitive with CL (81.92 \(\pm\) 0.14) and significantly outperformed FedAvg and FedProx (Figure 5). In terms of execution time, FL showed a 29.7% reduction (3001.1 \(\pm\) 85.2) compared to CL (4268.8 \(\pm\) 151), demonstrating that a distributed client environment is more efficient than analyzing large amounts of data at once. This assessment was conducted in a simulation environment, and results may vary in a real-world distributed client setting. However, similar execution times can be expected with stable network conditions. Furthermore, the performance of the proposed FL approach showed a 27.12% decrease in execution time compared to existing FL algorithms (maximum 4117.7 \(\pm\) 107 s).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics** **(95\% CI)**} & \multirow{2}{*}{**Centralized Learning**} & \multicolumn{4}{c}{**Federated Learning**} \\ \cline{3-6}  & & **LMECS** & **LMECS** & **LMECS** & \\  & & **(Loss)** & **(Accuracy)** & **(AUC)** & **FedAvg** \\ \hline Accuracy (\%) & 81.16 \(\pm\) 0.29 & 80.82 \(\pm\) 0.24 & **81.56 \(\pm\) 1.63** & 81.09 \(\pm\) 1.25 & 80.32 \(\pm\) 1.92 \\ Sensitivity (\%) & **84.02 \(\pm\) 0.16** & 83.03 \(\pm\) 0.34 & 83.81 \(\pm\) 0.33 & 83.78 \(\pm\) 2.22 & 79.13 \(\pm\) 1.73 \\ Specificity (\%) & 81.15 \(\pm\) 0.35 & 80.97 \(\pm\) 0.37 & 82.45 \(\pm\) 2.76 & 81.57 \(\pm\) 2.28 & **83.23 \(\pm\) 2.58** \\ PPV (\%) & **24.30 \(\pm\) 0.53** & 23.78 \(\pm\) 0.53 & 23.58 \(\pm\) 0.45 & 23.49 \(\pm\) 0.48 & 22.11 \(\pm\) 0.46 \\ NPV (\%) & **98.53 \(\pm\) 0.05** & 98.44 \(\pm\) 0.12 & 98.45 \(\pm\) 0.05 & 98.45 \(\pm\) 0.04 & 96.25 \(\pm\) 0.22 \\ AUC (\%) & **81.92 \(\pm\) 0.14** & 81.33 \(\pm\) 0.21 & 81.18 \(\pm\) 0.26 & 81.28 \(\pm\) 0.21 & 75.96 \(\pm\) 1.34 \\ Execution Time (s) & 4268.8 \(\pm\)151 & **3001.1 \(\pm\) 85.2** & 3291 \(\pm\) 83.2 & 3278.4 \(\pm\) 69.4 & 3207.6 \(\pm\) 101 \\ \hline \hline \end{tabular} Bold denotes the highest performance in each Metrics.  
\end{table}
Table 4: Comparison of global model performance between centralized learning and federated learning.  
Figure 3: Accuracy performance comparison between LMECS and existing FL algorithms.