  Unsupervised image-to-image translation methods such as CycleGAN learn to
convert images from one domain to another using unpaired training data sets
from different domains. Unfortunately, these approaches still require centrally
collected unpaired records, potentially violating privacy and security issues.
Although the recent federated learning (FL) allows a neural network to be
trained without data exchange, the basic assumption of the FL is that all
clients have their own training data from a similar domain, which is different
from our image-to-image translation scenario in which each client has images
from its unique domain and the goal is to learn image translation between
different domains without accessing the target domain data. To address this,
here we propose a novel federated CycleGAN architecture that can learn image
translation in an unsupervised manner while maintaining the data privacy.
Specifically, our approach arises from a novel observation that CycleGAN loss
can be decomposed into the sum of client specific local objectives that can be
evaluated using only their data. This local objective decomposition allows
multiple clients to participate in federated CycleGAN training without
sacrificing performance. Furthermore, our method employs novel switchable
generator and discriminator architecture using Adaptive Instance Normalization
(AdaIN) that significantly reduces the band-width requirement of the federated
learning. Our experimental results on various unsupervised image translation
tasks show that our federated CycleGAN provides comparable performance compared
to the non-federated counterpart.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning