  Federated learning enables building a shared model from multicentre data
while storing the training data locally for privacy. In this paper, we present
an evaluation (called CXR-FL) of deep learning-based models for chest X-ray
image analysis using the federated learning method. We examine the impact of
federated learning parameters on the performance of central models.
Additionally, we show that classification models perform worse if trained on a
region of interest reduced to segmentation of the lung compared to the full
image. However, focusing training of the classification model on the lung area
may result in improved pathology interpretability during inference. We also
find that federated learning helps maintain model generalizability. The
pre-trained weights and code are publicly available at
(https://github.com/SanoScience/CXR-FL).
