Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning, differential privacy and GANs can be used to work with private data without giving away a users' privacy. Recently, two implementations of such combinations have been published: DP-Fed-Avg GAN and GS-WGAN. This paper compares their performance and introduces an alternative version of DP-Fed-Avg GAN that makes use of denoising techniques to combat the loss in accuracy that generally occurs when applying differential privacy and federated learning to GANs. We also compare the novel adaptation of denoised DP-Fed-Avg GAN to the state-of-the-art implementations in this field.
翻译:在过去几十年里,计算机科学的几乎所有领域都应用了机器学习。引入GAN系统,在医学研究和文本预测领域提供了新的可能性。然而,这些新的领域利用了更加对隐私敏感的数据。为了维护用户隐私,可以使用联合学习、差异隐私和GAN系统结合使用私人数据,而不会泄露用户的隐私。最近,公布了两种应用这种组合的方法:DP-Fed-Avg GAN和GS-WGAN。本文比较了它们的表现,并介绍了一种DP-Fed-Avg GAN的替代版本,它利用分层技术来消除在对GAN系统应用差异隐私和联合学习时通常出现的准确性损失。我们还比较了对已取消的DP-Fed-Avg GAN系统进行创新的调整,使之适应该领域的最新实施。