Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data.

ABSTRACT:

Background

Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration.

Methods

In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving

SUBMITTER: Fang C 

PROVIDER: S-EPMC10884342 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature