Federated learning in healthcare: a benchmark comparison of engineering and statistical approaches for structured data analysis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was to bridge this gap with the first comprehensive comparison of FL frameworks from both domains. Methods: We assessed 7 FL frameworks, encompassing both engineering-based and statistical FL algorithms, and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator (Lasso). Our evaluation utilized both simulated data and real-world emergency department data, focusing on comparing both estimated model coefficients and the performance of model predictions. Results: The findings reveal that statistical FL algorithms produce much less biased estimates of model coefficients. Conversely, engineering-based methods can yield models with slightly better prediction performance, occasionally outperforming both centralized and statistical FL models. Conclusion: This study underscores the relative strengths and weaknesses of both types of methods, providing recommendations for their selection based on distinct study characteristics. Furthermore, we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.
Date Issued
2024-01
Date Acceptance
2024-09-13
Citation
Health Data Science, 2024, 4
ISSN
2765-8783
Publisher
American Association for the Advancement of Science (AAAS)
Journal / Book Title
Health Data Science
Volume
4
Copyright Statement
© 2024 Siqi Li et al.
Exclusive licensee Peking University Health Science Center. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
Exclusive licensee Peking University Health Science Center. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
License URL
Identifier
https://doi.org/10.34133/hds.0196
Publication Status
Published
Article Number
0196
Date Publish Online
2024-12-04