Federated horizontally partitioned principal component analysis for biomedical applications.

ABSTRACT:

Motivation

Federated learning enables privacy-preserving machine learning in the medical domain because the sensitive patient data remain with the owner and only parameters are exchanged between the data holders. The federated scenario introduces specific challenges related to the decentralized nature of the data, such as batch effects and differences in study population between the sites. Here, we investigate the challenges of moving classical analysis methods to the federated domain, specifically principal component analysis (PCA), a versatile and widely used tool, often serving as an initial step in machine learning and visualization workflows. We provide implementations of different federated PCA algorithms and evaluate them regarding their accuracy for high-dimensional biologi

SUBMITTER: Hartebrodt A 

PROVIDER: S-EPMC9710634 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature