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

In regions lacking medically certified causes of death, verbal autopsy (VA) is a 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 distribution shift across populations. Most existing VA algorithms rely on centralized training, requiring full access to training data for joint modeling. This can be 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 supports individual-level cause-of-death classification and population-level quantification of cause-specific mortality fractions 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 base 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 scenarios. Our results show that BFL significantly outperforms single-domain base models and performs comparably to or better than joint modeling.