Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.

ABSTRACT: Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data that are only available to that server and aggregate the model (not data) information at the global level. While federated learning has made significant advancements for machine learning methods such as deep neural networks, to the best of our knowledge, its development in sparse Bayesian models is still lacking. Sparse Bayesian models are highly interpretable with natural uncertain quantification, a desirable property for many scientific problems. However, without a federated learning algorithm, their applicability to sensitive biological/biomedical data from m

SUBMITTER: Kidd B 

PROVIDER: S-EPMC9782716 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature