Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking

Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking

In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA,...

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Bibliographic Details
Main Authors: Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Journal of Sensor and Actuator Networks
Subjects:
federated learning
blockchain
privacy
machine learning
encryption
Online Access:https://www.mdpi.com/2224-2708/14/1/11
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