Siqi Li

Objective: We propose FedIMPUTE, a communication-efficient federated learning (FL) based approach for missing value imputation (MVI). Our method enables multiple sites to collaboratively perform MVI in a privacy-preserving manner, addressing challenges of data-sharing constraints and population heterogeneity. Methods: We begin by conducting MVI locally at each participating site, followed by the application of various FL strategies, ranging from basic to advanced, to federate local MVI models without sharing site-specific data. The federated model is then broadcast and used by each site for MVI. We evaluate FedIMPUTE using both simulation studies and a real-world application on electronic health records (EHRs) to predict emergency department (ED) outcomes as a proof of concept. Results: Simulation studies show that FedIMPUTE outperforms all baseline MVI methods under comparison, improving downstream prediction performance and effectively handling data heterogeneity across sites. By using ED datasets from three hospitals within the Duke University Health System (DUHS), FedIMPUTE achieves the lowest mean squared error (MSE) among benchmark MVI methods, indicating superior imputation accuracy. Additionally, FedIMPUTE provides good downstream prediction performance, outperforming or matching other benchmark methods. Conclusion: FedIMPUTE enhances the performance of downstream risk prediction tasks, particularly for sites with high missing data rates and small sample sizes. It is easy to implement and communication-efficient, requiring sites to share only non-patient-level summary statistics.

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 seven 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.

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations.We searched 5 databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from 3 primary perspectives, including data quality, modeling strategies, and FL frameworks.Out of the 1193 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis.The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.