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Found 4 papers, 2 papers with code
Federated Bayesian Network Ensembles
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.
VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values
For structure learning we adapted the widely used K2 algorithm with a privacy-preserving scalar product protocol.
Privacy preserving n-party scalar product protocol
Privacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data.
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent
We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.
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