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_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.
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Acknowledgement
This work was supported by the Hainan Provincial Natural Science Foundation of China (Grant No. 2019RC041 and 2019RC098), Research and Application Project of Key Technologies for Blockchain Cross-chain Collaborative Monitoring and Traceability for Large-scale Distributed Denial of Service Attacks, National Natural Science Foundation of China (Grant No. 61762033), Opening Project of Shanghai Trusted Industrial Control Platform (Grant No. TICPSH202003005-ZC), and Education and Teaching Reform Research Project of Hainan University (Grant No. hdjy1970).
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Yan, B. et al. (2021). Experiments of Federated Learning for COVID-19 Chest X-ray Images. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_4
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DOI: https://doi.org/10.1007/978-3-030-78618-2_4
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