边缘云计算中基于生成对抗网络的联邦学习COVID-19检测
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing
摘要：
COVID-19在全球迅速蔓延，成为致命的大流行病。最近，许多基于人工智能的方法已用于COVID-19检测，但它们通常需要与云计算中心共享公共数据，因此仍然存在隐私问题。本文提出了一种新的联邦学习方案，称为FedGAN，用于生成逼真的COVID-19图像，以促进边缘云计算中使用生成对抗网络（GANs）进行隐私增强的COVID-19检测。特别地，我们首先提出了一种GAN，其中在每个基于边缘的医疗机构处交替地训练基于卷积神经网络（CNN）的卷积器和生成器，以模拟真实的COVID-19数据分布。然后，我们提出了一种新的联邦学习解决方案，允许本地GANs与云服务器协作并交换学习的参数，旨在丰富全局GAN模型，以生成逼真的COVID-19图像，而无需共享实际数据。为加强COVID-19联合数据分析的隐私保护，我们在每个医院机构整合了差异化隐私解决方案。此外，我们提出了一个新的基于区块链的FedGAN框架，用于安全的COVID-19数据分析，通过使用新的挖掘解决方案分散FL过程，以降低运行延迟。仿真结果证明了我们的方法在COVID-19检测方面优于最先进的方案。
英文摘要：
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.
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