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Machine learning has been applied to almost all fields of computer scienceover the past decades. The introduction of GANs allowed for new possibilitiesin fields of medical research and text prediction. However, these new fieldswork with ever more privacy-sensitive data. In order to maintain user privacy,a combination of federated learning, differential privacy and GANs can be usedto work with private data without giving away a users' privacy. Recently, twoimplementations of such combinations have been published: DP-Fed-Avg GAN andGS-WGAN. This paper compares their performance and introduces an alternativeversion of DP-Fed-Avg GAN that makes use of denoising techniques to combat theloss in accuracy that generally occurs when applying differential privacy andfederated learning to GANs. We also compare the novel adaptation of denoisedDP-Fed-Avg GAN to the state-of-the-art implementations in this field.

Using Autoencoders on Differentially Private Federated Learning GANs