# Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment

arXiv: https://arxiv.org/abs/2308.11220 · HTML: https://mlsanity.com/paper/2308.11220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 62 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 20 of 20)

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- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
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- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
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- [An Improved Analysis of Stochastic Gradient Descent with Momentum](https://mlsanity.com/paper/2007.07989/) (2020)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [FedAvg with Fine Tuning: Local Updates Lead to Representation Learning](https://mlsanity.com/paper/2205.13692/) (2022)
- [Secure Aggregation for Federated Learning in Flower](https://mlsanity.com/paper/2205.06117/) (2022)
- [A Review of Anonymization for Healthcare Data](https://mlsanity.com/paper/2104.06523/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data](https://mlsanity.com/paper/2011.14164/) (2020)
- [FedPerm: Private and Robust Federated Learning by Parameter Permutation](https://mlsanity.com/paper/2208.07922/) (2022)

## Abstract (arXiv)

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.