# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 4 Results  
To evaluate the performance of the federated strategies, we first analyse the performance of each strategy on a specific client in detail, along with the performance on the outsider test dataset. Afterwards, we analyse the performance of the federated strategies on a global overview across the various clients. Table 2 shows the strategies' performance on Client 3's local and outsider test datasets. We highlight in orange the best performance between the centralized and the local-only models--representing the non-federated scenario--and the best-performing strategies overall are marked with a green highlight. All federated strategies, except FedYogi, performed similarly, with only marginal differences. Despite FedYogi presenting similar convergence to those of the remaining strategies, the strategy diverged in one of the variations, labelling every image as a negative case. This divergence was likely caused by a loss explosion due to a higher server learning rate parameter, \(\eta=0.01\), as we observed non-divergence with a lower server learning rate, \(\eta=0.001\), albeit with lower performance than the other strategies. The variations to the hyperparameter of FedProx (\(\mu\)) did not result in significant differences in performance, and the best-performing value for the proximal term varied depending on what client and what dataset were to be used. Nevertheless, based on our experiments, this hyperparameter optimization for FedProx can improve the performance by 1%, and in other scenarios, it would require another search for the optimal value. Despite faster convergence on the training and validation datasets, the local-only model's performance is lower than that of all of federated strategies. The performance gain from the federation ranges from a marginal difference for clients with high-quality data, such as Client 1, to a boost of 2-6% as observed in Client 3, which contains lower-quality images, on both experiments. These gains can be seen in Table 3, where the best-performing strategy is marked with a green highlight for each client and experiment. Another noteworthy metric to analyse is the performance of the different strategies on the local test dataset and the outsider test dataset. From this, we can identify the strategies that fit better into a more heterogeneous environment from those that better fit into a more controlled environment. In Tables 2 and 3, we can notice two trends:  
1. FedBN tends to achieve higher performance on the local test dataset than other strategies. This is noticeable in the second experiment, where the distributions across the clients are similar and the heterogeneity of the datasets comes from the varying quality (i.e., due to different capturing equipment). This scenario aligns with the objective of FedBN, which uses the batch normalization layers kept as local-only information to alleviate feature shifts. However, this strategy falls behind in the experiment with multiple types of heterogeneity, where the data's distribution and quality varied across every client. 2. FedSGD and FedProx achieve the most generalizable models in both experiments. FedSGD achieves this by aggregating the weights more often and thus preventing the models from diverging too much from the global model. This comes at the cost of larger training time due to the increased time spent communicating the model. FedProx achieves the same result by adding the proximal term, which prevents the model from diverging too much from the global model. However, the FedProx approach does not result in increased communication costs. Despite not achieving the best performance, FedAvg remained close to every strategy in all scenarios, making it a viable choice in scenarios where there is a lack of information regarding the data without sacrificing performance or increasing training costs. Additionally, we also observed that adding a quality filter for training did not significantly affect the performance of the models in either direction and can be useful to cut down training times,