mlsanityData to 7 Oct 2026

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

arXiv 2308.11220 ↗First posted 22 Aug 2023cs.LG cs.AI cs.CRcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 62 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

20 of 62 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2019Fair Resource Allocation in Federated Learningcited by 389
  10. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  11. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  12. 2020An Improved Analysis of Stochastic Gradient Descent with Momentumcited by 117
8 more
  1. 2018A Review of Challenges and Opportunities in Machine Learning for Healthcited by 67
  2. 2021Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Networkcited by 57
  3. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
  4. 2022Secure Aggregation for Federated Learning in Flowercited by 15
  5. 2021A Review of Anonymization for Healthcare Datacited by 12
  6. 2022Federated learning: Applications, challenges and future directionscited by 11
  7. 2020Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Datacited by 7
  8. 2022FedPerm: Private and Robust Federated Learning by Parameter Permutationcited by 3

Built on by

Not cited yet.