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 datacentres and thus remain privacy concerns. This paper proposes a new federated learning 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 federated learning 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.

翻译：COVID-19已经在全球迅速扩散,成为致命的流行病。最近,许多人工智能方法已经用于COVID-19的检测,但它们往往需要与云层数据中心共享公共数据,从而仍然成为隐私问题。本文件提议一个新的联合学习计划,名为FedGAN, 产生现实的COVID-19的图像,用边缘云计算中的基因对抗网络(GANs)促进隐私增强COVID-19的检测。特别是,我们首先提议GAN, 在那里,每个边端医疗机构都使用一个导导师和一个基于神经网络的发电机来进行检测,或者训练,以模拟真实的COVID-19数据发布。然后,我们提出一个新的联合学习方案,让当地GAN与云服务器合作和交流学到的参数,目的是用全球GAN模型来更新现实的COVID-19图像,而不必分享实际数据。为了提高FD-19州数据分析的隐私,我们在每个医院机构都采用了一种差异隐私解决方案。此外,我们提议一个新的FD-D-19级数据探测系统,一个安全化的CA-D-C-CAN的升级化数据系统,以展示一个安全的CRUD-C-C-C-RD-C-C-C-C-C-C-CISl-RD-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-S-S-S-S-S-S-S-S-S-S-S-S-S-R-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-