论文标题
联盟学习的联合学习实验
Experiments of Federated Learning for COVID-19 Chest X-ray Images
论文作者
论文摘要
AI在Covid-19识别中起重要作用。计算机视觉和深度学习技术可以帮助确定使用胸部X射线图像的Covid-19感染。但是，为了保护和尊重患者的隐私，医院的特定医疗相关数据不允许未经许可泄漏和共享。收集此类培训数据是一个主要挑战。在一定程度上，这在执行深度学习方法检测Covid-19时缺乏足够的数据样本。联合学习是解决此问题的可用方法。它可以有效地解决数据孤岛的问题，并在不获得本地数据的情况下获得共享模型。在工作中，我们建议将联合学习用于COVID-19数据培训和部署实验以验证有效性。我们还将四个流行模型（Mobilenet，Resnet18，Moblienet和Covid-net）的表演与联合学习框架进行了比较，没有框架。这项工作旨在激发有关联邦学习的更多研究，以了解Covid-19。
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.
