Experiments of Federated Learning for COVID-19 Chest X-ray Images

Yan, B.; Wang, J.; Cheng, J.; Zhou, Y.; Zhang, Y.; Yang, Y.; Liu, L.; Zhao, H.; Wang, C.; Liu, B.

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_v2, ResNet18, ResNeXt, 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. © 2021, Springer Nature Switzerland AG.