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.

我们探讨了联邦集成贝叶斯网络在一系列实验中的应用，与本地训练模型和使用 VertiBayes 进行训练的模型进行了性能比较，结果表明 FBNE 在绝大多数情况下优于本地模型，在保持类似性能的同时显著提高了训练速度，是联邦学习工具箱中潜在有用的工具，特别适用于局部人口偏见严重或各方人口规模不平衡的情况。我们还讨论了该方法在时间复杂度、模型准确性、隐私保护和模型可解释性方面的优缺点。