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

TL;DR

This paper explores the application of federated learning to train COVID-19 detection models on chest X-ray images while preserving patient privacy, comparing different models' performances with and without federated learning.

Contribution

It demonstrates the effectiveness of federated learning in COVID-19 image classification and compares multiple models within this framework.

Findings

Federated learning improves data privacy in COVID-19 image analysis.

Performance of models varies with federated learning implementation.

The study encourages further research on federated learning for medical imaging.

Abstract

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.