  Since 2014, the NIH funded iDASH (integrating Data for Analysis,
Anonymization, SHaring) National Center for Biomedical Computing has hosted
yearly competitions on the topic of private computing for genomic data. For one
track of the 2020 iteration of this competition, participants were challenged
to produce an approach to federated learning (FL) training of genomic cancer
prediction models using differential privacy (DP), with submissions ranked
according to held-out test accuracy for a given set of DP budgets. More
precisely, in this track, we are tasked with training a supervised model for
the prediction of breast cancer occurrence from genomic data split between two
virtual centers while ensuring data privacy with respect to model transfer via
DP. In this article, we present our 3rd place submission to this competition.
During the competition, we encountered two main challenges discussed in this
article: i) ensuring correctness of the privacy budget evaluation and ii)
achieving an acceptable trade-off between prediction performance and privacy
budget.
