# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 7 Conclusions  
FL approaches show great potential in digital healthcare as they ensure a more secure environment when working with sensitive data. Research on this topic has been rapidly growing in the past years, and we expect it to grow even further with frameworks such as NVIDIA Flare [34], which aims to provide a scalable framework for federated learning, along with new algorithms to generate more optimal global models.  
In this paper, we aimed to study various FL algorithms and analyse the impact of heterogeneous clients on the performance of a federated approach to glaucoma diagnosis. For this, we conducted various experiments using public datasets of retinal fundus images from different sources to create clients with different characteristics. The model developed by [PERSON] et al. [24] was used as the base model for the federated setup. We evaluated different FL algorithms in these settings and analysed the results obtained.  
We concluded that the use of federated learning in this area of application resulted in an improvement in the model performance over the local-only approach while also achieving comparable performance to the centralized model without requiring the sharing of sensitive data, supporting federated learning as a viable method for enabling privacy-preserving machine learning without negatively impacting the capabilities of the trained models. Additionally, we extracted some scenarios where a specific FL algorithm is more suitable to be deployed, given the objective of the model and the nature of the data of the clients in the federation. In a scenario where the model's generalisation is crucial, FedProx would be a suitable choice based on our experiments and results achieved on the outsider datasets.  
**Author Contributions:** Conceptualization, F.S.; methodology, T.B., F.S. and C.S.; software, T.B.; validation, T.B. and F.S.; formal analysis, T.B., F.S. and C.S.; investigation, T.B. and F.S.; data curation, T.B. and F.S.; writing--review and editing, T.B., F.S., C.S. and T.O.; supervision, F.S. and C.S.; project administration, F.S.; funding acquisition, T.O. and F.S. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the project Transparent Artificial Medical Intelligence (TAMI) co-funded by Portugal 2020 framed under the Operational Programme for Competitiveness and Internationalisation (COMPETE 2020), Fundacao para a Ciencia and Technology (FCT), Carnegie Mellon University, and the European Regional Development Fund under grant NORTE-01-0247-FEDER-045905.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** All retinal fundus images used in this study belong to public datasets. This data can be found in the aforementioned references.  
The authors would like to thank [PERSON] for helping with the hardware setup.  
The authors declare that they have no competing interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ FL & Federated Learning \\ FedAvg & Federated Averaging \\ FedBN & Federated Batch Normalization \\ FedSDG & Federated Stochastic Gradient Descent \\ OCT & Optical Coherence Tomography \\ RFP & Retinal Fundus Photograph \\ SSL & Secure Sockets Layer \\ \end{tabular}