DsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.

ABSTRACT:

Motivation

In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources.

Results

Here, we describe the development of 'dsMTL', a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of the

SUBMITTER: Cao H 

PROVIDER: S-EPMC9620828 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature