Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome TreatmentDownload PDF

03 Oct 2022 (modified: 10 Jun 2024)Neurips 2022 SyntheticData4MLReaders: Everyone
Keywords: federated learning, machine learning, FedAvg, FedAvgM, FedProx, FedAdam, polycystic ovary syndrome, data privacy
TL;DR: We demonstrate that a variety of federated learning approaches succeed on a synthetic PCOS patient dataset to identify the most effective treatment option.
Abstract: 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. Our code is open-sourced at https://github.com/toriqiu/fl-pcos.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2308.11220/code)
4 Replies

Loading