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.

翻译：女性内分泌学领域在数据驱动的医疗解决方案上长期滞后，主要源于对患者数据隐私的担忧。关于激素水平或月经周期的宝贵数据点可能暴露合并症患者或终止妊娠者的信息，侵犯其隐私。本研究探索应用联邦学习（FL）预测多囊卵巢综合征（PCOS）患者的最佳用药方案。PCOS是一种影响全球数百万女性的严重激素紊乱疾病，但其病理机制尚不明确，且因患者数据匮乏导致研究受阻。我们证明多种联邦学习方法在合成PCOS患者数据集上取得良好效果。所提出的联邦学习模型既能访问海量多样化数据以识别最有效的治疗方案，又能为PCOS患者提供隐私保障。