Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks. (arXiv:2207.00263v1 [cs.CR])

A Generative Adversarial Network (GAN) is a deep-learning generative model in
the field of Machine Learning (ML) that involves training two Neural Networks
(NN) using a sizable data set. In certain fields, such as medicine, the
training data may be hospital patient records that are stored across different
hospitals. The classic centralized approach would involve sending the data to a
centralized server where the model would be trained. However, that would
involve breaching the privacy and confidentiality of the patients …

arxiv encryption federated learning generative adversarial networks homomorphic encryption learning networks performance training