# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 1 Introduction  
In digital healthcare, machine learning and, more specifically, deep learning (DL) models have seen growth in their descriptive and predictive capabilities. DL models, particularly convolutional neural networks, have been widely explored for medical imaging to process and analyze various imaging techniques, such as MRI [1], CT scans [2], X-rays [3] and ultrasounds [4]. Physicians use these images to detect and diagnose numerous diseases like cancer and cardiovascular and eye diseases. In ophthalmology, retinal fundus photography (RFP) and, more recently, optical coherence tomography (OCT) are used to screen for the main eye diseases--diabetic retinopathy, glaucoma and age-related macular degeneration [5]. The vast amount of data and the diversity of the diseases make it difficult for physicians to analyse the images manually. Therefore, developing these ML models has become increasingly important for assisting physicians and improving the diagnosis process. However, DL models rely on access to a large amount of data, often centralized, which is not always feasible. Hospitals and institutions must comply with the General Data Protection Regulation (GDPR) and other privacy regulations, which restrict the sharing of patient data [6]. Additionally, approval by institutional review boards is required to determine to what extent data can be shared with peers, and anonymization of data is often required. Data anonymization is a complex and time-consuming task and needs to ensurethat patient identification cannot be obtained from the data. Nevertheless, studies have already shown the possibility of reconstruction of identification from anonymized data [7], resulting in a critical liability regarding the privacy of sensitive information. In this context, there is a need for solutions that enable the development of ML models in a distributed, privacy-preserving manner. Federated Learning (FL) is a machine learning paradigm that enables the development of ML models in a distributed manner without data sharing. To achieve this, each federation participant, often called a client, trains a model on its local data. Afterwards, each client sends the model weights to a central server, which aggregates the various local models received from the clients to generate a global model. For this reason, FL has been gaining traction in digital healthcare as one of the privacy-preserving methods for artificial intelligence (AI) in medical applications, as it enables the possibility of training a model that learns on data from various data centres without requiring any of the data to leave each data centre's network. Furthermore, FL can be further complemented with other privacy-preserving techniques, such as differential privacy [8], which enable a higher degree of privacy of the sensitive data. The server uses the FL algorithm or federated strategy to generate the global model, which combines the clients' model weights. For example, one possible algorithm is to perform a weighted average of the clients' model weights, which denotes the Federated Average (FedAvg) algorithm [9]. One of the biggest challenges in FL stands on the heterogeneity of the clients, which can be caused by differences in the types of data, amounts of data, quality of data, etc. In this context, FL algorithms must adapt to the heterogeneity of the clients, and multiple algorithms and variations have been proposed to tackle this challenge. As such, choosing the FL algorithm is a crucial step that needs to be considered and depends on the type of heterogeneity present. However, there is a gap in the literature regarding the extent to which these algorithms are affected by the heterogeneity of the clients, whether in terms of data distribution, data quality or other factors.