  Although deep federated learning has received much attention in recent years,
progress has been made mainly in the context of natural images and barely for
computational pathology. However, deep federated learning is an opportunity to
create datasets that reflect the data diversity of many laboratories. Further,
the effort of dataset construction can be divided among many. Unfortunately,
existing algorithms cannot be easily applied to computational pathology since
previous work presupposes that data distributions of laboratories must be
similar. This is an unlikely assumption, mainly since different laboratories
have different staining styles. As a solution, we propose BottleGAN, a
generative model that can computationally align the staining styles of many
laboratories and can be trained in a privacy-preserving manner to foster
federated learning in computational pathology. We construct a heterogenic
multi-institutional dataset based on the PESO segmentation dataset and improve
the IOU by 42\% compared to existing federated learning algorithms. An
implementation of BottleGAN is available at
https://github.com/MECLabTUDA/BottleGAN
