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

Han Cao, Youcheng Zhang, Jan Baumbach, Paul R. Burton, Dominic B. Dwyer, Nikolaos Koutsouleris, Julian O. Matschinske, Yannick Marcon, Sivanesan Rajan, Thilo Rieg, Patricia Ryser-Welch, Julian Späth, Carl Herrmann, Emanuel Schwarz

Abstract: 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.