# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 5 Discussion  
During the study, the researchers addressed all research questions stated in the beginning. The results demonstrate the effectiveness of the proposed FL architecture.  
(_R_1) The proposed FL architecture leverages collaborative learning across multiple clients while preserving data privacy and addressing the sensitive nature of medical datasets like fundus images. By utilizing the VGG-19 model with TL, the architecture ensures high compatibility with the feature space common across clients. The k-client selection method further enhances efficiency by enabling only a subset of clients (randomly selected \(k\) = 2) to train locally per round, reducing resource requirements while maintaining the consistency of global model updates. This approach ensures the collaborative training process captures shared patterns across all clients, even with non-IID data distributions. The architecture thus effectively demonstrates FL's potential for enabling secure and efficient collaboration in eye healthcare without requiring data centralization and the violation of any data-sharing and data privacy regulations.  
(R2) The results indicate that the proposed FL architecture achieves a high level of accuracy comparable to CL. The _DataWeightedFed_ approach achieved 84.88% accuracy, just a 1.85% reduction compared to the centralized VGG-19 model's accuracy of 86.63%. Conventional FL approaches often lose up to 55% accuracy due to non-IID data and insufficient aggregation strategies. By incorporating the dynamically weighted _wFedAvg_ method, the proposed _DataWeightedFed_ approach minimizes this loss by accounting for dataset size in aggregation, improving the representation of client models in the global update. The convergence of the training loss and accuracy for both FL and CL models (Figure 6) further highlights that the FL architecture maintains competitive performance while adhering to FL principles of privacy and decentralization. However, it is important to note that there may be slight computational costs for having dynamically calculated weights to adjust for newly added clients for the architecture, as the architecture will have to remain open at each round of FL.  
(R3) The computational efficiency of the proposed FL architecture is a direct result of the following design choices: the k-client selection method ensures that only \(k=2\) clients are trained per round, reducing computational overhead while allowing clients to train in parallel. Each client trains for just one epoch per round (\(n=1\)), and the global model is updated after every round. This design makes rounds and epochs nearly synonymous, ensuring that the FL training time matches the centralized training time. For example, the FL architecture completed five training rounds with k-client selection, equivalent to five epochs of centralized training. The architecture dynamically updates the weights (\(\eta_{j}\)) in the _wFedAvg_ aggregation as new clients join the system, making it scalable to larger datasets and more clients without sacrificing speed or accuracy. These features demonstrate that the computational load is effectively distributed across clients while maintaining an overall training speed equivalent to centralized models.