  We introduce FELICIA (FEderated LearnIng with a CentralIzed Adversary) a
generative mechanism enabling collaborative learning. In particular, we show
how a data owner with limited and biased data could benefit from other data
owners while keeping data from all the sources private. This is a common
scenario in medical image analysis where privacy legislation prevents data from
being shared outside local premises. FELICIA works for a large family of
Generative Adversarial Networks (GAN) architectures including vanilla and
conditional GANs as demonstrated in this work. We show that by using the
FELICIA mechanism, a data owner with limited image samples can generate
high-quality synthetic images with high utility while neither data owners has
to provide access to its data. The sharing happens solely through a central
discriminator that has access limited to synthetic data. Here, utility is
defined as classification performance on a real test set. We demonstrate these
benefits on several realistic healthcare scenarions using benchmark image
datasets (MNIST, CIFAR-10) as well as on medical images for the task of skin
lesion classification. With multiple experiments, we show that even in the
worst cases, combining FELICIA with real data gracefully achieves performance
on par with real data while most results significantly improves the utility.
