# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 2 Related Work  
In a setting where data sharing is heavily regulated for ethical and legal reasons, centralising patient data to develop medically significant machine learning models is often impossible or has several limitations. FL is one of the approaches to address these issues. Several studies on FL applied to the medical field have been done. [PERSON] et al. [10] developed a federated approach to breast density classification using a weighted averaging strategy (FedAvg) and compared results against locally trained models at each institution. The federated model obtained better agreement on classifications between institutions than the models trained on local data. However, the model's performance is still not in an acceptable state to be used due to the heterogeneity of the data across the institutions. One of the possible reasons is the strategy used, as FedAvg assumes uniformly distributed data across the institutions. Thus, this research could be complemented with other strategies to address this issue. [PERSON] et al. (2018) apply FL for COVID-19 detection using X-ray images and information about the symptoms. The authors study the performance of the FL model in various settings, such as IID and non-IID settings, with varying numbers of participants and underlying DL models. The authors also combine FL with the differential privacy method to achieve a more secure system while evaluating model performance trade-offs. FL has also been applied to brain tumour segmentation ([PERSON] et al., 2018; [PERSON] et al., 2018). [PERSON] et al. (2018) evaluate the performance of FL comparatively to a data-sharing centralized approach and other collaborative ML approaches. In their work, the authors show the potential of FL over other collaborative ML approaches in a real-world setting. Similarly, [PERSON] et al. (2018) also evaluated the performance of FL comparatively to a centralized approach. Furthermore, the authors study the impact of the percentage of the model shared on the performance. In ophthalmology, [PERSON] et al. (2018) evaluate the performance of FL for microvasculature segmentation and diabetic retinopathy detection using OCT and OCTA en face images. The authors study and validate the potential of FL to adapt to settings with a small amount of data available across clients. FL has also been applied to RFP ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). [PERSON] et al. (2018) evaluate the performance of FL compared to centralized learning for detecting retinopathy of prematurity (ROP) using fundus images. In the same domain, [PERSON] et al. (2018) propose an FL approach that generates the diagnosis's vascular severity score (VSS). The authors studied the variations in the VSS for the different categories of ROP. In this study, the authors found significant differences in the severity scores between the early stage and the advanced stage of ROP despite not showing any variance when comparing the intermediate stage with the advanced stage. [PERSON] et al. (2018) investigate the vulnerability of FL to gradient inversion attacks. These attacks exploit the fact that gradients carry some information about the data that influenced their calculation, and manipulation of the model's input can enable the extraction of information regarding the training data. In the paper, the authors evaluate the possibility of such attacks for reconstructing RFP images from an FL model used to classify diabetic retinopathy. The authors show the possibility of reconstructing clinical features to identify an individual from gradient information transmitted during the federation. While these studies provide a great foundation for the potential of FL in heterogeneous environments for medical imaging, several challenges still need to be addressed. First, the performance of DL models trained on local data is often not inferior to that of FL models.