【Author】
Nguyen, Dinh C.; Ding, Ming; Pathirana, Pubudu N.; Seneviratne, Aruna; Zomaya, Albert Y.
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud data centers and thus, remain privacy concerns. This article proposes a new federated learning (FL) scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new FL solution, which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes.
【Keywords】COVID-19; Training; Generative adversarial networks; Data models; Hospitals; Servers; Pandemics; COVID-19; edge cloud; federated learning (FL); generative adversarial network (GAN)
【标题】边缘云计算中基于生成对抗网络的COVID-19检测联邦学习
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