  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.
