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

arXiv: https://arxiv.org/abs/2505.02257 · HTML: https://mlsanity.com/paper/2505.02257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 18 of 18)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [Personalized Federated Learning with Gaussian Processes](https://mlsanity.com/paper/2106.15482/) (2021)
- [Personalized Federated Learning via Variational Bayesian Inference](https://mlsanity.com/paper/2206.07977/) (2022)
- [FedPop: A Bayesian Approach for Personalised Federated Learning](https://mlsanity.com/paper/2206.03611/) (2022)
- [Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors](https://mlsanity.com/paper/2006.12335/) (2020)
- [Bayesian Federated Learning: A Survey](https://mlsanity.com/paper/2304.13267/) (2023)
- [Federated Learning with Uncertainty via Distilled Predictive Distributions](https://mlsanity.com/paper/2206.07562/) (2022)
- [Personalized Online Federated Learning with Multiple Kernels](https://mlsanity.com/paper/2311.05108/) (2023)
- [From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives](https://mlsanity.com/paper/2404.02438/) (2024)
- [Bayesian factorizations of big sparse tensors](https://mlsanity.com/paper/1306.1598/) (2013)
- [Modeling Structure and Country-specific Heterogeneity in Misclassification Matrices of Verbal Autopsy-based Cause of Death Classifiers](https://mlsanity.com/paper/2312.03192/) (2023)
- [Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy](https://mlsanity.com/paper/2112.10978/) (2021)
- [Hierarchical Latent Class Models for Mortality Surveillance Using Partially Verified Verbal Autopsies](https://mlsanity.com/paper/2410.09274/) (2024)
- [Flexible Bayesian Tensor Decomposition for Verbal Autopsy Data](https://mlsanity.com/paper/2502.00171/) (2025)

## Abstract (arXiv)

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.