Federated Bayesian Network Ensembles
Florian van Daalen Lianne Ippel Andre Dekker Inigo Bermejo
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.