Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift

TL;DR

This paper introduces a Bayesian Federated Learning framework for verbal autopsy data that maintains privacy, handles distribution shifts, and improves cause-of-death classification without sharing raw data across sources.

Contribution

It presents a novel federated learning approach tailored for verbal autopsy data, enabling accurate cause-of-death analysis across diverse populations without data sharing.

Findings

BFL outperforms single-domain models in various distribution shift scenarios.

BFL achieves comparable or better results than joint modeling on real VA datasets.

The framework is modular, efficient, and compatible with existing VA algorithms.

Abstract

In regions lacking medically certified causes of death, verbal autopsy (VA) is a critical and widely used tool to ascertain the cause of death through interviews with caregivers. Data collected by VAs are often analyzed using probabilistic algorithms. The performance of these algorithms often degrades due to distributional shift across populations. Most existing VA algorithms rely on centralized training, requiring full access to training data for joint modeling. This is often infeasible due to privacy and logistical constraints. In this paper, we propose a novel Bayesian Federated Learning (BFL) framework that avoids data sharing across multiple training sources. Our method enables reliable individual-level cause-of-death classification and population-level quantification of cause-specific mortality fractions (CSMFs), in a target domain with limited or no local labeled data. The proposed framework is modular, computationally efficient, and compatible with a wide range of existing VA algorithms as candidate models, facilitating flexible deployment in real-world mortality surveillance systems. We validate the performance of BFL through extensive experiments on two real-world VA datasets under varying levels of distribution shift. Our results show that BFL significantly outperforms the base models built on a single domain and achieves comparable or better performance compared to joint modeling.