Federated Proportional Likelihood Ratio Model for Heterogeneous Multisite Studies

Music City Center 

Federated learning enables collaborative data analysis while preserving privacy, making it particularly valuable in multisite healthcare studies where data sharing is restricted. The proportional likelihood ratio model (PLRM) is a flexible semi-parametric framework used in these settings, often assuming a common regression coefficient β across sites. However, real-world differences in population characteristics and study protocols can lead to slight variations in β. To address this, we develop a federated learning method that allows for minor variations in β while still leveraging global information to improve estimation efficiency at a primary site. Unlike existing methods that focus on site-specific nuisance parameters, our approach explicitly models and accounts for β heterogeneity, enhancing robustness in distributed inference.

Federated Learning

Semi-parametric Methods

Proportional Likelihood Ratio Model

Distributed Inference

Heterogeneous Data Analysis

Multisite Studies 

Biometrics Section