Privacy-preserving federated machine learning on FAIR health data: A real-world application

Privacy-preserving federated machine learning on FAIR health data: A real-world application

Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative...

Full description

Saved in:
Bibliographic Details
Main Authors: A. Anil Sinaci, Mert Gencturk, Celia Alvarez-Romero, Gokce Banu Laleci Erturkmen, Alicia Martinez-Garcia, María José Escalona-Cuaresma, Carlos Luis Parra-Calderon
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Privacy-preserving machine learning
Federated machine learning
FAIR data
Distributed datasets
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037024000382
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2001037024000382

Similar Items