p23 / Journal
Federated learning for privacy-friendly health apps: a case study on ovulation tracking
Journal of Sensor and Actuator Networks 14(1), 11 · 2025
In plain language
FLORA explores how ovulation tracking can combine useful predictions with stronger control over sensitive health information.
Research context
It connects privacy research with the practical architecture and user needs of a reproductive health application.
Approach
The application architecture combines local federated training, privacy technologies, blockchain consent records, and participation incentives.
Findings
The paper presents an integrated design for keeping personal records on devices while coordinating collaborative model updates and auditable consent.
Summary based on abstract.