论文标题
在差异私人联合学习gan上使用自动编码器
Using Autoencoders on Differentially Private Federated Learning GANs
论文作者
论文摘要
在过去的几十年中，机器学习几乎已应用于计算机科学的所有领域。引入gan允许在医学研究和文本预测领域的新可能性。但是，这些新领域与对隐私敏感的数据有效。为了维护用户隐私，可以使用联合学习，差异隐私和gans的结合来处理私人数据，而无需赠送用户的隐私。最近，已经发布了两种此类组合的实现：DP-FED-AVG GAN和GS-WGAN。本文比较了他们的性能，并介绍了DP-FED-AVG GAN的替代版本，该版本利用Denoisising技术来应对准确性的损失，而准确性损失通常在应用差异隐私和联合学习到GAN时通常会发生。我们还比较了DENOCED DP-FED-AVG GAN的新颖适应与该领域的最新实现。
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.
