# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 5 Discussion  
In this section, we summarise and address the results obtained, considering the objectives of this paper. Firstly, we analysed the convergence of the various strategies and observed a similar pattern for all strategies except for FedYogi, which diverged in one experiment and had a significantly slower convergence in the other. We also observed that the centralized and local-only models converge faster than all the federated models and that early stopping does not negatively affect the performance of the models.  
Despite this, we later observed that the performance of the local-only model on the test datasets was lower than that of most federated strategies, especially on clients whose local data were comprised of lower-quality images. From our results, we can answer our first objective (a) in Section 1, concluding that employing a federated approach does pose a benefit to the model performance and its generalizability compared to training a model with only the client's local data, as it essentially allows the model to learn from a larger pool of data while not compromising the privacy of such data. This approach can also benefit clients with higher-quality datasets, albeit the differences in those cases are more marginal. And while the centralized approach still had the overall best performance, federated approaches could reach performances close to that of the centralized method while maintaining data security and isolation in their own client's network.  
Furthermore, we also analysed the performance differences of each strategy for each of the experiments, which allows us to answer our second objective (b) in Section 1. We observed that the model's performance is lower if trained in a more heterogeneous environment, as seen in Client 1's tests, the data for which remained identical across experiments. However, eliminating lower-quality images from the training process to achieve a more consistent quality distribution across clients does not always benefit the model's performance, as we observed in our experiments. With this, the impact caused by having clients with diverse distributions during training can degrade the model performance, as observed with Client 1, with some strategies being more affected than others.  
Indeed, the last observation from our experiments is the adaptability of specific strategies to each context, which answers our final objective of the paper (c) in Section 1. We observed that FedBN generally performs the best given a scenario wherein the federated model's goal is to optimise the performance on the client's local data given that those clients follow similar distributions with their data. On the other hand, FedSGD and FedProx exhibited higher performance under scenarios wherein the clients' data are more diverse. These strategies also showed higher generalizability, as we can observe by the performances on the outsider dataset. Based on the results, we conclude that FedSGD and FedProx are the most suitable strategies in scenarios for which the clients' data are more diverse or if said distributions are unknown. This choice also applies if the objective is to obtain a more generalizable model. On the other hand, we opt to choose FedBN to optimise the model for the client's local data. FedAvg poses a solid choice in both scenarios, albeit not optimal.