  When it comes to preserving privacy in medical machine learning, two
important considerations are (1) keeping data local to the institution and (2)
avoiding inference of sensitive information from the trained model. These are
often addressed using federated learning and differential privacy,
respectively. However, the commonly used Federated Averaging algorithm requires
a high degree of synchronization between participating institutions. For this
reason, we turn our attention to Private Aggregation of Teacher Ensembles
(PATE), where all local models can be trained independently without
inter-institutional communication. The purpose of this paper is thus to explore
how PATE -- originally designed for classification -- can best be adapted for
semantic segmentation. To this end, we build low-dimensional representations of
segmentation masks which the student can obtain through low-sensitivity queries
to the private aggregator. On the Brain Tumor Segmentation (BraTS 2019)
dataset, an Autoencoder-based PATE variant achieves a higher Dice coefficient
for the same privacy guarantee than prior work based on noisy Federated
Averaging.
