Privacy-Preserving Transfer Learning Approach

Music City Center 

The underrepresentation of diverse populations in clinical research undermines the generalizability of predictive models, particularly for groups with small sample sizes. Data-sharing restrictions across institutions further complicate collaborative analysis. To address this issue, we propose META-TL, a federated transfer learning framework that integrates heterogeneous data from multiple healthcare institutions to improve predictive accuracy for underrepresented target populations. META-TL leverages informative features, handles high-dimensional data efficiently, reduces computational costs, and maintains robust performance. Theoretical analysis and simulations show META-TL performs comparably to pooled analysis, despite data-sharing constraints, and remains resilient to noisy or biased data. We demonstrate its practical utility by applying it to electronic health records (EHR) for predicting type 2 diabetes risk in underrepresented groups.

transfer learning

privacy-preserving

feature selection

high-dimensional data