Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment
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.
Code (1)
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
In-depth Analysis of Privacy Threats in Federated Learning for Medical Data
Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with priv…
Federated LearningFedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning, and post-operative assessment. With the…
Federated LearningImage SegmentationPrivacy PreservingSemantic Segmentation+1FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection
The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy ris…
Contrastive LearningFederated LearningPrivacy PreservingPrivacy-preserving patient clustering for personalized federated learning
Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation i…
ClusteringFederated LearningMortality PredictionPersonalized Federated Learning+1Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulati…
Federated Learning