  AI plays an important role in COVID-19 identification. Computer vision and
deep learning techniques can assist in determining COVID-19 infection with
Chest X-ray Images. However, for the protection and respect of the privacy of
patients, the hospital's specific medical-related data did not allow leakage
and sharing without permission. Collecting such training data was a major
challenge. To a certain extent, this has caused a lack of sufficient data
samples when performing deep learning approaches to detect COVID-19. Federated
Learning is an available way to address this issue. It can effectively address
the issue of data silos and get a shared model without obtaining local data. In
the work, we propose the use of federated learning for COVID-19 data training
and deploy experiments to verify the effectiveness. And we also compare
performances of four popular models (MobileNet, ResNet18, MoblieNet, and
COVID-Net) with the federated learning framework and without the framework.
This work aims to inspire more researches on federated learning about COVID-19.
