## 4 Distributed Model Training in a Clinical Study
### Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Accuracy** & **Bal. Accuracy** & **Precision** & **Recall** & **F1-Score** & **Matt. Cor. Coef.** \\ \cline{2-7}  & **R/S/S1000** & **R/S/S1000** & **R/S/S1000** & **R/S/S1000** & **R/S/S1000** & **R/S/S1000** \\ \hline SST & 92.5/91.4/92.7 & 90.3/91.1/92.2 & 98.6/87.5/90.2 & 81.2/89.8/90.2 & 89.1/88.6/90.2 & 84.4/81.7/84.4 \\ (UKA) & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.020/0.010/0.010 & \(\pm\)0.010/0.020/0.010 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.020/0.020/0.020 \\ \hline SST & 88.6/93.4/91.2 & 84.8/92.4/91.0 & 100.0/93.8/86.9 & 69.5/88.3/90.2 & 82.0/90.9/88.5 & 76.7/85.9/81.5 \\ (UMG) & \(\pm\)0.000/0.010/0.010 & \(\pm\)0.000/0.010/0.010 & \(\pm\)0.000/0.020/0.020 & \(\pm\)0.010/0.020/0.020 & 0.010/0.020/0.020 & 0.010/0.020/0.020 \\ \hline SST & 92.7/96.8/94.2 & 91.1/96.7/93.9 & 95.9/25.0/91.5 & 84.8/96.5/93.0 & 89.7/95.7/92.2 & 84.4/93.7/87.6 \\ (UKK) & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.030/0.010/0.020 & 0.020/0.010/0.010 & 0.020/0.020/0.020 \\ \hline SST & 94.7/93.1/94.2 & 94.1/92.2/95.0 & 94.4/93.1/87.5 & 91.4/88.3/98.4 & 92.9/90.6/92.7 & 88.7/85.3/88.3 \\ (UKL) & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.020/0.020 & \(\pm\)0.010/0.020/0.000 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.010/0.020/0.020 \\ \hline SST & 94.2/91.4/94.7 & 93.0/92.5/94.7 & 95.7/83.0/91.7 & 88.3/96.9/94.5 & 91.8/89.4/93.1 & 87.5/82.9/88.9 \\ (IMISE) & \(\pm\)0.020/0.010/0.000 & \(\pm\)0.020/0.010/0.000 & \(\pm\)0.020/0.020/0.010 & \(\pm\)0.040/0.010/0.020 & \(\pm\)0.030/0.020/0.000 & \(\pm\)0.040/0.030/0.000 \\ \hline SST & 92.1/88.7/94.0 & 90.9/90.7/93.5 & 92.4/77.5/92.5 & 85.9/98.4/91.4 & 89.1/86.7/91.9 & 83.0/78.9/87.2 \\ (HSM) & \(\pm\)0.000/0.010/0.000 & \(\pm\)0.000/0.000/0.000 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.000/0.000/0.010 & \(\pm\)0.000/0.010/0.000 & \(\pm\)0.010/0.010/0.010 \\ \hline IIIL & 93.3/90.2/95.6 & 92.2/91.9/95.6 & 93.8/86.0/93.1 & 87.9/98.4/95.3 & 90.7/88.3/94.2 & 85.6/81.3/90.7 \\  & \(\pm\)0.000/0.010/0.000 & \(\pm\)0.000/0.010/0.000 & \(\pm\)0.010/0.020/0.010 & \(\pm\)0.010/0.000/0.010 & \(\pm\)0.000/0.010/0.000 & \(\pm\)0.010/0.020/0.010 \\ \hline CIIIL & 93.0/93.7/94.6 & 91.4/94.6/95.0 & 95.6/86.9/89.5 & 85.2/98.0/96.9 & 90.1/92.1/93.1 & 85.0/87.4/88.8 \\  & \(\pm\)0.010/0.000/0.010 & \(\pm\)0.010/0.000/0.000 & \(\pm\)0.010/0.010/0.010 & \(\pm\)0.020/0.010/0.000 & \(\pm\)0.010/0.000/0.010 & \(\pm\)0.020/0.010/0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results in non-IID setting (unequally-sized and unbalanced class distribution). The meaning of the abbreviations are: real-world data (R), synthetic data (S) and synthetic data with the generation size of 1000 (S1000). Overall, synthetic data can not achieve the same results as real data while training on a single site (SST). For IIL and CIIIL, the model trained with synthetic data in a large generation size achieved higher accuracy, recall and F1-score but lower precision, especially in a non-IID environment. Our experiments show that the data generation is also influenced by the present data bias at each institution and cannot alleviate its influence on the model training. Nevertheless, the more synthetic data is involved the more stable the model performance across each institution. In the next section, we will discuss the role of the on-boarding service in this study.