  Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS)
MRIs from non-FS proton density sequences has the potential to accelerate
acquisition of knee MRIs. However, GANs trained on single-site data have poor
generalizability to external data. We show that federated learning can improve
multi-center generalizability of GANs for synthesizing FS MRIs, while
facilitating privacy-preserving multi-institutional collaborations.
