A comprehensive experimental comparison between federated and centralized learning.

ABSTRACT: Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks. We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various dat

SUBMITTER: Garst S 

PROVIDER: S-EPMC11928227 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature