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

Differentially Private Federated Learning for Cancer Prediction