  Purpose: We apply federated learning to train an OCT image classifier
simulating a realistic scenario with multiple clients and statistical
heterogeneous data distribution where data in the clients lack samples of some
categories entirely.
  Methods: We investigate the effectiveness of FedAvg and FedProx to train an
OCT image classification model in a decentralized fashion, addressing privacy
concerns associated with centralizing data. We partitioned a publicly available
OCT dataset across multiple clients under IID and Non-IID settings and
conducted local training on the subsets for each client. We evaluated two
federated learning methods, FedAvg and FedProx for these settings.
  Results: Our experiments on the dataset suggest that under IID settings, both
methods perform on par with training on a central data pool. However, the
performance of both algorithms declines as we increase the statistical
heterogeneity across the client data, while FedProx consistently performs
better than FedAvg in the increased heterogeneity settings.
  Conclusion: Despite the effectiveness of federated learning in the
utilization of private data across multiple medical institutions, the large
number of clients and heterogeneous distribution of labels deteriorate the
performance of both algorithms. Notably, FedProx appears to be more robust to
the increased heterogeneity.
