Federated Bayesian Network Ensembles

• URL: http://arxiv.org/abs/2402.12142v1
• Date: Mon, 19 Feb 2024 13:52:37 GMT
• Title: Federated Bayesian Network Ensembles
• Authors: Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo
• Abstract summary: Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. We show that FBNE is a potentially useful tool within the federated learning toolbox. We discuss the advantages and disadvantages of this approach in terms of time complexity, model accuracy, privacy protection, and model interpretability.
• Score: 3.24530181403525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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