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Traditionally, Bayesian network structure learning is often carried out at acentral site, in which all data is gathered. However, in practice, data may bedistributed across different parties (e.g., companies, devices) who intend tocollectively learn a Bayesian network, but are not willing to discloseinformation related to their data owing to privacy or security concerns. Inthis work, we present a federated learning approach to estimate the structureof Bayesian network from data that is horizontally partitioned across differentparties. We develop a distributed structure learning method based on continuousoptimization, using the alternating direction method of multipliers (ADMM),such that only the model parameters have to be exchanged during theoptimization process. We demonstrate the flexibility of our approach byadopting it for both linear and nonlinear cases. Experimental results onsynthetic and real datasets show that it achieves an improved performance overthe other methods, especially when there is a relatively large number ofclients and each has a limited sample size.

Towards Federated Bayesian Network Structure Learning with Continuous Optimization