# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 3 Methodology
### Project Dataset  
This study utilized the Ocular Disease Intelligent Recognition (ODIR) dataset from [PERSON] et al., which consists of 10,000 colored fundus photographs taken from patients' left and right eyes [30]. The dataset is designed to simulate real-world data, containing non-independent and identically distributed (non-IID) images captured using various types of cameras, resulting in different image resolutions and quality. The diversity of images in these data will accurately reflect the challenges faced when using data from various collaborating locations (e.g., hospitals). The dataset is divided into eight different fundus  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Ref.** & **Methodology** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2023)} & \multirow{3}{*}{[18]} & CL with voting ensemble of ResNet50, VGG-19, AlexNet, DNS201, IncRes & \multirow{3}{*}{High accuracy (85.43\%)} & Lacks privacy-preserving techniques (PPTs), not applicable in real-world privacy-restricted environments \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2019)} & \multirow{3}{*}{[19]} & CL using a single layer perceptron model & Practical approach using smartphones, good accuracy (85\%) & Limited complexity, lacks generalizability, no FL support \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2024)} & \multirow{3}{*}{[20]} & CL and TL with MobileNetV1 and V2 & High accuracy (89\%) with TL & Models designed for smaller datasets, not scalable to large real-world scenarios \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2023)} & \multirow{3}{*}{[23]} & FL with CNN ensemble architectures for brain tumor classification & Demonstrates FL’s effectiveness in medical imaging & Accuracy reduction compared to non-FL methods (from 96.68\% to 91.05\%) \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2019)} & \multirow{3}{*}{[24]} & FL for brain tumor segmentation with privacy protection & Analyzes trade-offs between accuracy and privacy in FL & Increased differential privacy noise lowers model performance \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2023)} & CL using VGG-16 for lung cancer classification & High sensitivity (92.08\%) and accuracy (91\%) & Lacks PPT, limited to single neural network architecture \\ \hline \multirow{3}{*}{[PERSON] and [PERSON]} & \multirow{3}{*}{(2021)} & \multirow{3}{*}{[26]} & Deep learning (DL) with CNN for fundus image classification & Effective in detecting multiple eye diseases & Does not incorporate PPT, potential overfitting due to limited dataset \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2023)} & \multirow{3}{*}{[27]} & \multirow{3}{*}{FL for cataract disease detection using CNN} & Preserves data privacy, demonstrates FL’s applicability in medical imaging & Reduction in accuracy compared to centralized methods, requires robust communication infrastructure \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2022)} & \multirow{3}{*}{[28]} & Centralized DL using a multi-categorical neural network for retinal image classification & Demonstrated feasibility of multiple multiple retinal diseases with overfitting, lacks PPT \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{(2022)} & \multirow{3}{*}{[29]} & Centralized DL using CNNs for medical image analysis & Achieved high accuracy in detecting specific medical conditions & Requires large, labeled datasets, lacks PPT, potential generalization issues \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between previous, related works.  
disease categories: normal, diabetes, hypertension, glaucoma, cataract, age-related macular degeneration (AMD), myopia, and other abnormalities, as depicted in Figure 2: