Using Autoencoders on Differentially Private Federated Learning GANs. (arXiv:2206.12270v1 [cs.LG])

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: …

lg