# Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach

arXiv: https://arxiv.org/abs/2108.12112 · HTML: https://mlsanity.com/paper/2108.12112/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier](https://mlsanity.com/paper/1906.02903/) (2019)
- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [Predicting with Proxies: Transfer Learning in High Dimension](https://mlsanity.com/paper/1812.11097/) (2018)
- [Heterogeneity-aware and communication-efficient distributed statistical inference](https://mlsanity.com/paper/1912.09623/) (2019)
- [Distributed Inference for Linear Support Vector Machine](https://mlsanity.com/paper/1811.11922/) (2018)
- [Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints](https://mlsanity.com/paper/2004.00816/) (2020)
- [Communication-efficient Distributed Sparse Linear Discriminant Analysis](https://mlsanity.com/paper/1610.04798/) (2016)

## Built on by (most cited first, 20 of 20)

- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Multiply Robust Federated Estimation of Targeted Average Treatment Effects](https://mlsanity.com/paper/2309.12600/) (2023)
- [Multi-Source Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2405.09331/) (2024)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Multi-Task Learning with Summary Statistics](https://mlsanity.com/paper/2307.02388/) (2023)
- [Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)](https://mlsanity.com/paper/2211.16557/) (2022)
- [Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis](https://mlsanity.com/paper/2407.11034/) (2024)
- [LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation](https://mlsanity.com/paper/2412.20605/) (2024)
- [Transfer learning for tensor Gaussian graphical models](https://mlsanity.com/paper/2211.09391/) (2022)
- [Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction](https://mlsanity.com/paper/2405.15600/) (2024)
- [Learning with Shared Representations: Statistical Rates and Efficient Algorithms](https://mlsanity.com/paper/2409.04919/) (2024)
- [Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees](https://mlsanity.com/paper/2410.08934/) (2024)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)
- [Structural transfer learning of non-Gaussian DAG](https://mlsanity.com/paper/2310.10239/) (2023)
- [Multivariate and Online Transfer Learning with Uncertainty Quantification](https://mlsanity.com/paper/2411.12555/) (2024)
- [GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds](https://mlsanity.com/paper/2505.02972/) (2025)
- [Explaining Concept Shift with Interpretable Feature Attribution](https://mlsanity.com/paper/2505.20634/) (2025)
- [Formal Bayesian Transfer Learning via the Total Risk Prior](https://mlsanity.com/paper/2507.23768/) (2025)
- [Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning](https://mlsanity.com/paper/2606.05258/) (2026)
- [Hierarchical Projection for Adaptive Knowledge Transfer](https://mlsanity.com/paper/2606.08691/) (2026)

## Abstract (arXiv)

The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research has become a barrier to translating precision medicine research into practice. Due to heterogeneity across populations, risk prediction models are often found to be underperformed in these underrepresented populations, and therefore may further exacerbate known health disparities. In this paper, we propose a two-way data integration strategy that integrates heterogeneous data from diverse populations and from multiple healthcare institutions via a federated transfer learning approach. The proposed method can handle the challenging setting where sample sizes from different populations are highly unbalanced. With only a small number of communications across participating sites, the proposed method can achieve performance comparable to the pooled analysis where individual-level data are directly pooled together. We show that the proposed method improves the estimation and prediction accuracy in underrepresented populations, and reduces the gap of model performance across populations. Our theoretical analysis reveals how estimation accuracy is influenced by communication budgets, privacy restrictions, and heterogeneity across populations. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-center study, in which we construct polygenic risk prediction models for Type II diabetes in AA population.