Federated Bayesian Network Ensembles
<|reference_start|>Federated Bayesian Network Ensembles: Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. Ensemble-based learning works by training multiple (weak) classifiers whose output is aggregated. Federated ensembles are ensembles applied to a federated setting, where each classifier in the ensemble is trained on one data location.   In this article, we explore the use of federated ensembles of Bayesian networks (FBNE) in a range of experiments and compare their performance with locally trained models and models trained with VertiBayes, a federated learning algorithm to train Bayesian networks from decentralized data. Our results show that FBNE outperforms local models and provides a significant increase in training speed compared with VertiBayes while maintaining a similar performance in most settings, among other advantages. We show that FBNE is a potentially useful tool within the federated learning toolbox, especially when local populations are heavily biased, or there is a strong imbalance in population size across parties. We discuss the advantages and disadvantages of this approach in terms of time complexity, model accuracy, privacy protection, and model interpretability.<|reference_end|>
@article{van daalen2024federated,
    title={Federated Bayesian Network Ensembles},
    author={Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo},
    journal={arXiv preprint arXiv:2402.12142},
    year={2024},
    doi={10.1109/FMEC59375.2023.10306230},
    archivePrefix={arXiv},
    eprint={2402.12142},
    primaryClass={cs.LG cs.CR}
}