Federated multimodal learning with heterogeneous modality and distribution shift

Music City Center 

Federated learning enables the analysis of multi-site real-world data (RWD) while preserving data privacy, yet challenges persist due to heterogeneous modality availability and distribution shifts across sites. In this work, we develop a novel federated multimodal learning framework to improve causal inference in distributed research networks (DRNs), integrating electronic health records (EHRs) and genetic biomarkers. Traditional methods often fail to account for structural missingness and site-specific heterogeneity, leading to biased estimates of treatment effects.
To address this, we propose a new statistical framework that accounts for distribution shifts of populations across sites, while pursuing efficiency and bias correction by leveraging information from all available modalities across sites. In addition, we employ multiple negative control outcomes to calibrate estimates and mitigate residual systematic biases, including unmeasured confounding.

Causal inference

Negative control outcomes

Average treatment effect

Bias Correction

Multi-Modality 

Section on Statistical Learning and Data Science