## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Source** & **Approach** & \
\begin{tabular}{c} **Z-Test** \\ **(p-Value)** \\ \end{tabular} &
\begin{tabular}{c} **T-Test** \\ **(p-Value)** \\ \end{tabular} \\ \hline \multirow{8}{*}{1 st} & 1 st + 2 nd + 3 rd & Centralized & 0.073 & 0.090 \\  & 2 nd & Direct transfer & \textless{}0.001 \({}^{*}\) & 0.015 \({}^{*}\) \\  & 3 rd & Direct transfer & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 2 nd & Repurposed transfer & 0.109 & 0.164 \\  & 3 rd & Repurposed transfer & 0.710 & 0.732 \\  & 2 nd + 3 rd & Voting & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 1 st + 2 nd + 3 rd & Stacking & 0.021 \({}^{*}\) & 0.046 \({}^{*}\) \\  & 1 st + 2 nd + 3 rd & Federated & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{8}{*}{2 nd} & 1 st + 2 nd + 3 rd & Centralized & 0.623 & 0.569 \\  & 1 st & Direct transfer & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 3 rd & Direct transfer & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 1 st & Repurposed transfer & 0.106 & 0.120 \\  & 3 rd & Repurposed transfer & 0.623 & 0.087 \\  & 1 st + 3 rd & Voting & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 1 st + 2 nd + 3 rd & Stacking & 0.035 \({}^{*}\) & 0065 \({}^{*}\) \\  & 1 st + 2 nd + 3 rd & Federated & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{8}{*}{3 rd} & 1 st + 2 nd + 3 rd & Centralized & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 1 st & Direct transfer & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & 2 nd & Direct transfer & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \cline{1-1}  & 1 st & Repurposed transfer & 0.253 & 0.307 \\ \cline{1-1}  & 2 nd & Repurposed transfer & 0.317 & 0.368 \\ \cline{1-1}  & 1 st + 2 nd & Voting & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \cline{1-1}  & 1 st + 2 nd + 3 rd & Stacking & 0.101 & 0.136 \\ \cline{1-1}  & 1 st + 2 nd + 3 rd & Federated & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\) Significant differences.  
\end{table} 
Table 2: Test results. performance in most cases, regardless of differences in data size. But when the target dataset was the smallest (third dataset), there were significant differences. Looking at the stacking strategy, Table 2 shows that there were significant differences when comparing with the individual model with the first and second datasets as targets. In that case, stacking outperformed the baseline. But when the target dataset was the smallest (third dataset), there were no significant differences. In this case, the stacking approach produced a very similar performance with no significant differences. Finally, repurposed transfer did not produce substantial differences in any of the cases, indicating that its performance was almost indistinguishable from the local models, even when the entities worked with different size datasets. Thus, it produced a very good performance in AUC on the three datasets or entities without any significant differences from the baseline models.