# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Local Performance**} & \multicolumn{6}{c}{**Outsider Performance**} \\ \hline
**Strategy** & **Fit Time (h)** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Specificity** \\ \hline Baseline & - & 0.407 & 0.781 & 0.382 & 0.513 & 0.521 & 0.459 & 0.444 & 0.326 & 0.376 & 0.591 \\ Local-Only & **1.8** & 0.850 & 0.899 & **0.920** & 0.909 & 0.534 & 0.754 & **0.712** & 0.854 & 0.777 & **0.655** \\ Centralized & 3.9 & **0.870** & **0.931** & 0.908 & **0.920** & **0.699** & **0.779** & 0.711 & **0.940** & **0.810** & 0.618 \\ FedAvg & 2.2 & 0.860 & **0.928** & 0.908 & 0.913 & **0.685** & 0.761 & 0.693 & 0.934 & 0.796 & 0.587 \\ FedSGD & 3.3 & 0.860 & 0.925 & 0.902 & 0.913 & 0.671 & 0.763 & **0.695** & **0.939** & **0.799** & **0.588** \\ FedBN & 2.2 & **0.868** & **0.928** & 0.908 & **0.918** & **0.685** & 0.760 & 0.692 & 0.936 & 0.796 & 0.584 \\ FedProx x0 & 2.3 & 0.863 & **0.928** & 0.902 & 0.915 & **0.685** & 0.760 & 0.693 & 0.933 & 0.795 & 0.587 \\ \(\mu=1.0\) & 2.3 & 0.858 & 0.925 & 0.899 & 0.912 & 0.671 & 0.764 & 0.697 & 0.933 & 0.798 & 0.595 \\ FedProx x2 & 2.3 & 0.858 & 0.925 & 0.899 & 0.912 & 0.671 & 0.758 & 0.692 & 0.930 & 0.793 & 0.585 \\ \(\mu=0.1\) & & & & & & & & & & & \\ FedProx x0 & 2.4 & & & & & & & & & & \\ FedProx x1 & 2.4 & 0.752 & 0.837 & 0.865 & 0.851 & 0.247 & 0.534 & 0.52 & 0.894 & 0.658 & 0.174 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the different strategies on the local and outsider test datasets. In orange, the best non-federated approach. In green, the best federated approach. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Dataset-HD**} & \multicolumn{6}{c}{**Dataset-QD**} \\ \hline
**Strategy** & **Filter** & **Client 1** & **Client 2** & **Client 3** & **Outsider** & **Client 1** & **Client 2** & **Client 3** & **Outsider** \\ \hline Baseline & - & 0.368 & 0.522 & 0.530 & 0.468 & 0.368 & 0.472 & 0.407 & 0.459 \\ \hline \multirow{2}{*}{Local-Only} & ✗ & 0.825 & 0.752 & 0.738 & 0.776 2 & 0.827 & 0.817 & 0.850 & 0.754 2 \\  & ✓ & 0.820 & 0.738 & 0.760 & 0.783 2 & 0.822 & 0.813 & 0.822 & 0.728 2 \\ \hline \multirow{2}{*}{Centralized} & ✗ & **0.842** & **0.777** & **0.803** & **0.839** & **0.873** & 0.832 & 0.870 & **0.729** \\  & ✓ & 0.832 & 0.760 & **0.803** & 0.837 & 0.868 & **0.842** & **0.873** & 0.771 \\ \hline \multirow{2}{*}{FedAvg} & ✗ & **0.827** & 0.762 & 0.772 & 0.810 & 0.866 & 0.827 & 0.860 & 0.761 \\  & ✓ & 0.815 & 0.748 & 0.762 & 0.809 & 0.884 & 0.820 & 0.863 & 0.759 \\ \hline \multirow{2}{*}{FedSGD} & ✗ & 0.825 & 0.762 & **0.788** & **0.817** & 0.860 & **0.830** & 0.860 & 0.763 \\  & ✓ & **0.827** & 0.748 & 0.765 & **0.817** & 0.848 & 0.825 & 0.863 & **0.764** \\ \hline \multirow{2}{*}{FedBN} & ✗ & 0.820 & 0.762 & 0.772 & 0.810 & 0.868 & **0.830** & **0.868** & 0.760 \\  & ✓ & 0.820 & 0.750 & 0.760 & 0.810 & 0.845 & 0.817 & 0.863 & 0.760 \\ \hline \multirow{2}{*}{FedProx 1} & ✗ & **0.827** & **0.772** & 0.777 & **0.817** & **0.873** & 0.827 & 0.863 & **0.764** \\  & ✓ & 0.817 & 0.755 & 0.767 & 0.807 & 0.850 & 0.825 & 0.863 & 0.758 \\ \hline \multirow{2}{*}{FedYogi 1} & ✗ & 0.705 & 0.613 & 0.485 & 0.580 & 0.772 & 0.740 & 0.752 & 0.534 \\  & ✓ & 0.700 & 0.610 & 0.468 & 0.569 & 0.767 & 0.735 & 0.740 & 0.528 \\ \hline \hline \end{tabular}  
* For federated strategies with multiple experiments, only the best is represented in the table. 2 The local-only performance on the outsider was obtained by evaluating each client’s local-only model on the outsider dataset. The model that obtained the best results is represented here.  
\end{table} 
Table 3: Accuracy achieved on the test datasets for each federated strategy. In orange, the best non-federated approach. In green, the best federated approach. as we observed up to 20% reduction in the total training time.