# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 2 Related Work  
Many studies have been conducted on the effectiveness of applying ML to improving disease diagnosis in healthcare, especially within ophthalmology and the eye healthcare space. [PERSON] et al. examined the detection of multi-stage glaucoma, including its many early stages [18]. The researchers did so by leveraging standard, centralized-learning-based, pretrained convolutional neural networks (CNNs) and voting-based classifier fusion. In doing so, they achieved an 84.53% accuracy on the Harvard Dataverse dataset. [PERSON] et al. proposed a practical method for detecting cataracts, one of the leading causes of visual impairment and blindness worldwide [19]. They applied a single-layer perceptron method and smartphones to classify the results of normal eyes, immature cataracts, and mature cataracts with an accuracy of 85%. [PERSON] et al. also demonstrated an effective system for cataracts and glaucoma detection [20]. It was accomplished by leveraging transfer learning (TL) using the MobileNetV1 and MobileNetV2 models, achieving a detection accuracy of 89%. All three of these studies show how ML and CL have been proven effective in transforming the effectiveness and precision of disease diagnosis, especially within the eye healthcare space. However, these models do not allow for secure, collaborative model training. Therefore, these proposed systems are ineffective in real-world environments with data privacy restrictions or regulations, where FL offers plenty of advantages. The application of FL in other fields has gained significant momentum in recent years. Several comprehensive surveys have provided overviews of FL and its challenges and applications within various domains. [PERSON] et al. presented a systematic study of recent advances in FL, discussing challenges such as data heterogeneity, communication overhead, and privacy concerns [21]. This highlights the idea that utilizing FL in any discipline hasadvantages and disadvantages that must be balanced. Similarly, [PERSON] al. offered an extensive review of FL, highlighting its potential applications and the challenges faced in practical implementations [22]. Work has also begun to discuss the application of FL in many fields, including healthcare and medical imaging. For instance, in medical imaging (particularly brain tumor detection), [PERSON] et al. showcased FL's effectiveness when combined with CNN ensemble architectures in classifying brain tumors from MRI images [23]. Their work demonstrated the potential of FL in healthcare, providing concrete evidence of how FL can facilitate and allow for collaborative learning without compromising the privacy of personal information (PI) patient data. However, as mentioned earlier, FL faces issues regarding accuracy reductions--in the study, the base ensemble model (non-FL approach) had an accuracy of 96.68%, while the FL achieved 91.05% accuracy. Although accuracy only had a slight decline, maintaining accuracy in a field as critical and crucial as healthcare is still of utmost importance. Observations can also be made based on the work completed by [PERSON] et al., which leveraged FL to address the medical data privacy regulations when training models for brain tumor segmentation specifically [24]. Based on their work, it was noted that there was a trade-off between model performance and level of privacy protection. It was found that, when more of the models are shared, the amount of noise that must be added to the data (through the differential privacy methods) must also increase, causing the model performance to decrease due to increased noise. Their work argues that sharing a smaller percentage of the models (e.g., 10% instead of 40%) can often yield a more favorable trade-off between having an optimal amount of privacy and the performance of the models in FL systems. This is especially important because it emphasizes how the optimal FL architecture must consider these drawbacks and advantages to effectively balance facets such as accuracy, training time, and privacy cost.