# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 4 Results  
To evaluate the performance of the proposed FL architecture, a singular, centralized VGG-19 model was trained for comparison purposes. This model was trained on all the data from all FL clients pooled together, resulting in a singular dataset. It used the same applicable hyperparameters as the models used in the FL architecture (one training epoch, batch size of 16, learning rate of 0.001, etc.). This model was used to compare the effectiveness of the proposed FL architecture to standard CL, as shown below. Figure 6 represents the training loss and accuracy in both approaches.  
Both Figures 6 and 7 show the convergence of the FL architecture global model compared to CL. As shown in both figures, both accuracy and loss reached similar values in the same training time. This means the FL architecture had little to no disadvantage in model performance and training efficiency compared to the centralized model counterpart. It is important to note that, although the FL architecture uses both rounds of FL and epochs to conduct client training, rounds and epochs are almost synonymous for this study. This is because FL architecture had clients trained for one epoch in each round of FL, and there were only five rounds of FL. Since each client is only trained for one epoch and the k clients selected are all _trained in parallel_, the FL simulation theoretically trains the same amount as the centralized models. This means that the amount of training completed on the FL global model is equivalent to the training conducted on the centralized model, which trained for five epochs while leveraging CL with just a singular dataset. The architecture can dynamically scale to accommodate many clients and a bigger dataset.  
The accuracy and accuracy reduction comparison between existing centralized models and this study's proposed FL architecture approach can be seen in Table 2.  
As can be seen from Table 2, the results outperform other findings [18; 19] and are slightly below the results of [20], which uses significantly less complex ML models MobileNetV1 and MobileNetV2, suitable only for a tiny dataset. As a result, the proposed novel FL architecture is effective. As shown in Table 2 above, the accuracy reduction was minuscule compared to regular CL models/architectures. The proposed FL approach of  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Learning** & **Model(s)** & **Accuracy** & **Reduction** \\ \hline
[18] & CL &
\begin{tabular}{c} Voting Ensemble of ResNet50, \\ VGG-19, AlexNet, DNS201, IncRes \\ \end{tabular} & 85.43\% & 0.64\% \\ \hline
[19] & CL & Single Layer Perceptron Model & 85.00\% & 0.14\% \\ \hline
[20] & CL, TL & MobileNetV1, MobileNetV2 & 89.00\% & 4.62\% \\ \hline Ours & CL & VGG-19 & 86.63\% & 2.02\% \\ \hline Ours & FL &
\begin{tabular}{c} VGG-19 (with \(w\)_FL_/_Avg_ and k-client \\ selection training) \\ \end{tabular} & 84.88\% & 1.85\% \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy and Accuracy Reduction comparison: published work vs. our approach.  
Figure 6: Training loss comparison between the centralized vs. the proposed FL architecture global model. As shown, convergence time is very similar between both CL and the proposed FL architecture global model.  
Figure 7: Accuracy comparison between the centralized vs. the proposed FL architecture global model. As shown, the accuracy is very similar for both CL and the proposed FL architecture global model and is also reached in the same amount of training time.  
VGG-19 TL with the _wFedAvg_ aggregator had only an average 1.85% accuracy decrease, compared to the conventional FL average of up to about a 55% accuracy decrease.