Federated Learning on Patient Data for Privacy-Protecting Polycystic
  Ovary Syndrome Treatment
Lucia Morris Tori Qiu Nikhil Raghuraman
The field of women's endocrinology has trailed behind data-driven medical
solutions, largely due to concerns over the privacy of patient data. Valuable
datapoints about hormone levels or menstrual cycling could expose patients who
suffer from comorbidities or terminate a pregnancy, violating their privacy. We
explore the application of Federated Learning (FL) to predict the optimal drug
for patients with polycystic ovary syndrome (PCOS). PCOS is a serious hormonal
disorder impacting millions of women worldwide, yet it's poorly understood and
its research is stunted by a lack of patient data. We demonstrate that a
variety of FL approaches succeed on a synthetic PCOS patient dataset. Our
proposed FL models are a tool to access massive quantities of diverse data and
identify the most effective treatment option while providing PCOS patients with
privacy guarantees.