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

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.

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Cited by in corpus (6)

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• Dynamic Fusion based Federated Learning for COVID-19 Detection
• Blockchain-based Trustworthy Federated Learning Architecture
• Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging