Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.
Journal:
Journal of the American Medical Informatics Association : JAMIA
Published Date:
May 1, 2019
Abstract
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way.