  Labelling data is expensive and time consuming especially for domains such as
medical imaging that contain volumetric imaging data and require expert
knowledge. Exploiting a larger pool of labeled data available across multiple
centers, such as in federated learning, has also seen limited success since
current deep learning approaches do not generalize well to images acquired with
scanners from different manufacturers. We aim to address these problems in a
common, learning-based image simulation framework which we refer to as
Federated Simulation. We introduce a physics-driven generative approach that
consists of two learnable neural modules: 1) a module that synthesizes 3D
cardiac shapes along with their materials, and 2) a CT simulator that renders
these into realistic 3D CT Volumes, with annotations. Since the model of
geometry and material is disentangled from the imaging sensor, it can
effectively be trained across multiple medical centers. We show that our data
synthesis framework improves the downstream segmentation performance on several
datasets. Project Page: https://nv-tlabs.github.io/fed-sim/ .
