# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 7 Study Limitations and Future Work  
Even though this study's proposed FL architecture, methods, and results look optimistic, there is still room for improvement. This study only used four clients due to technological and resource restrictions. However, when using FL in a real-life healthcare setting, the number of clients will rise dramatically to hundreds or even thousands, so testing this proposed architecture and evaluating the results when involving more clients will be essential. Although this study used a novel _FedAvg_ modification with dynamically adjusted weights, many other robust global model aggregation methods may also be effective when dealing with fundus disease image data. These other aggregation methods may reduce the accuracy trade-off that comes with privacy-preserving FL and differential privacy, so in the future, we aim to employ other methods to evaluate if the architecture performance will increase. Finally, although this study focused on applying FL to the eye healthcare space, the study's proposed architecture could be flexible enough to be applied to any domain where data privacy is of utmost concern. Therefore, in the future, we aim to evaluate this study's proposed architecture's performance within other fields and domains.  
Source code can be found at [[https://github.com/coolraycode/Enhanced-Privacy-Preserving-Architecture-for-Fundus-Disease-Diagnosis-with-Federated-Learning](https://github.com/coolraycode/Enhanced-Privacy-Preserving-Architecture-for-Fundus-Disease-Diagnosis-with-Federated-Learning)]([https://github.com/coolraycode/Enhanced-Privacy-Preserving-Architecture-for-Fundus-Disease-Diagnosis-with-Federated-Learning](https://github.com/coolraycode/Enhanced-Privacy-Preserving-Architecture-for-Fundus-Disease-Diagnosis-with-Federated-Learning)).  
Conceptualization, R.J.; methodology, R.J.; software, R.J.; validation, Y.K. and D.K.; formal analysis, R.J.; investigation, R.J.; resources, R.J.; data curation, R.J.; writing--original draft preparation, R.J. and Y.K.; writing--review and editing, Y.K. and D.K.; visualization, R.J.; supervision, Y.K. and D.K.; project administration, Y.K. and D.K.; funding, Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article and Supplementary Materials.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMD & Age-related Macular Degeneration \\ CL & Centralized Learning \\ CLAHE & Contrast Limited Adaptive Histogram Equalization \\ CNN & Convolutional Neural Network \\ DL & Deep Learning \\ FL & Federated Learning \\ GDPR & General Data Protection Regulation \\ HIPAA & Health Insurance Portability and Accountability Act \\ ML & Machine Learning \\ non-IID & non-Independent and Identically Distributed \\ ODIR & Ocular Disease Intelligent Recognition \\ PI & Personal Information \\ PIPEDA & Personal Information Protection and Electronic Documents Act \\ PPT & Privacy-Preserving Technique \\ TL & Transfer Learning \\ \end{tabular}