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Federated Bayesian Network Ensembles
Feb. 20, 2024, 5:42 a.m. | Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo
cs.LG updates on arXiv.org arxiv.org
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 …
abstract algorithms arxiv bayesian classifier classifiers concerns cs.cr cs.lg data data sharing decentralized decentralized data ensemble federated learning machine machine learning machine learning algorithms multiple network privacy training type
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