## 4 Experimental Design
### Environment Setup  
We performed FL simulations based on Ray [47] using the Flower framework [48] in a server environment equipped with an Intel(R) Core (TM) i9-10980 XE CPU (3.00 GHz) (Intel Corporation, Santa Clara, CA, USA) and two NVIDIA GeForce RTX 3090 GPUs (NVIDIA Corporation, Santa Clara, CA, USA). Additionally, we were able to monitor and observe the results of this experiment by utilizing the FedOps platform (version 1.1). [49; 50; 51], which can manage the FL lifecycle. The data processing and modeling were carried out using Pytorch (version 2.2.0). To facilitate unbiased and accurate comparative analysis across the three experimental environments, we defined a rigorous experimental design. Data from 50 randomly selected hospitals were used, and to ensure reliability and statistical significance, 10 trials were conducted for each set. Despite the random selection of hospitals, each experimental environment was structured with the same dataset to ensure comparability. The hyperparameter information for all experiments is presented in Table 2. The data configuration for each experiment is detailed in Table 3, and a non-IID data distribution was observed in all experimental environments. This thorough experimental setup ensured that the comparative analysis between standalone learning, centralized learning, and FL environments was robust, allowing for a comprehensive understanding of each approach in the context of emergency room patient severity analysis.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameter** & **Notation** & **Value** \\ \hline Number of CPUs per Client & \(c_{cpu}\) & 4 \\ Number of GPUs per Client & \(c_{gpu}\) & 0 \\ Server GPU Resources & \(s_{gpu}\) & 1 \\ Communication Round/Iteration & \(r\) & 100 \\ Number of Epochs per Round & \(\varepsilon\) & 2 \\ Batch Size & \(b\) & 1024 \\ Criterion for Loss & \(L\) & BinaryCross \\ Optimization & \(opt\) & Adam \\ Learning Rate & \(\eta\) & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter configuration used in the experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & \multicolumn{3}{c}{**Client Data (Train)**} & \multicolumn{3}{c}{**Evaluation Data (Test0)**} \\ \cline{2-6}
**Number** & **Non-Critical Care** & **Critical Care** & **Total Size** & **Non-Critical Care** & **Critical Care** & **Total Size** \\ \hline
1 & 93.2\% & 6.8\% & 3,866,342 & 93.09\% & 6.91\% & 773,268 \\
2 & 93.35\% & 6.65\% & 3,301,971 & 93.03\% & 6.97\% & 660,394 \\
3 & 93.71\% & 6.29\% & 3,492,681 & 92.94\% & 7.06\% & 698,536 \\
4 & 93.62\% & 6.38\% & 2,476,352 & 92.91\% & 7.09\% & 495,270 \\
5 & 93.8\% & 6.2\% & 3,574,151 & 92.83\% & 7.17\% & 714,830 \\
6 & 92.55\% & 7.45\% & 2,418,920 & 93.4\% & 6.6\% & 483,784 \\
7 & 94.1\% & 5.9\% & 3,595,783 & 92.74\% & 7.26\% & 719,156 \\
8 & 91.93\% & 8.07\% & 3,547,194 & 93.59\% & 6.41\% & 709,438 \\
9 & 93.4\% & 6.6\% & 2,665,113 & 93.05\% & 6.95\% & 544,022 \\
10 & 92.75\% & 7.25\% & 3,763,022 & 93.26\% & 6.74\% & 752,604 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-IID data configuration for each experiment.  
Figure 2: Experimental design of this study: (**a**) is standalone learning (**b**) is centralized learning (CL) and (**c**) is federated learning (FL). The blue frame is the test data for testing the client’s model, and the yellow frame is the data for testing the global model.