# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 3 Materials and Methods
### Federated Setup  
The federated setup comprises three identical Jetson AGX Xavier setups representing the three independent institutions in the federated network. The server of the federated network is also an institution independent from the three clients above. However, due to restrictions on the network, one of the Jetsons had to act as both a server and a client. Despite the restriction, this does not hinder any performance of the machine, and the server and client running on the same machine are still treated as independent processes. Each client only has access to its dataset located on the Jetson and has no access or information regarding any other clients' datasets. The server, meanwhile, has no access to any information on any of the clients' datasets other than any information communicated during federation, such as the number of samples trained. The model trained on each client is also only available to the client itself, and only the model weights, either totally or partially depending on the federated strategy, are shared with the server. For the model training, each dataset was divided into three splits: training (60%), validation (20%) and test (20%). The model developed by [PERSON] et al. [24] serves as the baseline and initiation model for the federated setup. The federation was implemented using Flower ([[https://flower.dev/](https://flower.dev/)]([https://flower.dev/](https://flower.dev/)) accessed on 22 October 2023) as the backend framework responsible for the communication protocols between the server and the clients, which is started by our API. In this study, we experimented with various federated strategies: FedAvg [9], FedSGD [9], FedProx [26], FedBN [27] and FedYogi [28]. The first two strategies are part of one of the initial studies done on FL and serve as the standard baseline for FL strategies. The remaining three were chosen as they have often been mentioned and used as benchmarks for multiple studies [29; 30; 31; 32]. FedAvg aggregates the clients' weights by performing a weighted average to generate the new global model. FedSGD works in the same way as FedAvg but only performs a single local epoch (step) in each federated round. FedProx introduces one key difference compared to FedAvg by adding a proximal term to the model optimizer in order to limit the divergence of the local model from the global model in each federated round. Similarly, FedBN introduces one key difference to FedAvg by excluding the batch normalization layers from being sent to the server for aggregation to alleviate feature shifts. Finally, FedYogi adapts the Yogi optimizer into a federated setting. With regards to the strategies' hyperparameters, we varied FedProx's proximal term (\(\mu\)) between 1.0, 0.01 and 0.1, following the values from the authors. FedYogi also had its server learning rate parameter (\(\eta\)) varied between 0.01 and 0.001. Alongside the federated approach, we also conducted experiments with non-federated approaches, for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Target** & **Dataset** & **Base** & **Complementary** & **Target** & **Target** & **Target** \\
**Dataset** & & **Dataset** & **Dataset** & **Samples** & **Negative Labels** & **Positive Labels** \\ \hline Client 1 & Dataset-HD & REFUGE & EyePACS AIROGS & 2000 & 80\% & 20\% \\ Dataset & Dataset-QD & REFUGE & EyePACS AIROGS & 2000 & 80\% & 20\% \\ \hline Client 2 & Dataset-HD & ORIGA + GAMMA & EyePACS AIROGS & 2000 & 50\% & 50\% \\ Dataset & Dataset-QD & ORIGA + GAMMA & EyePACS AIROGS & 2000 & 80\% & 20\% \\ \hline Client 3 & Dataset-HD & Drishti & EyePACS AIROGS & 2000 & 20\% & 80\% \\ Dataset & Dataset-QD & Drishti & EyePACS AIROGS & 2000 & 80\% & 20\% \\ \hline Outsider & Dataset-HD & EyePACS AIROGS & - & - & 50\% & 50\% \\ Dataset & Dataset-QD & EyePACS AIROGS & - & - & 50\% & 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data distribution parameters across the different clients.