Towards Federated Bayesian Network Structure Learning with Continuous Optimization
This addresses privacy concerns in distributed data settings for Bayesian network learning, but it is incremental as it adapts existing optimization methods to a federated context.
The paper tackles the problem of learning Bayesian network structures from data distributed across multiple parties without sharing raw data, by proposing a federated learning approach using continuous optimization and ADMM. Experimental results show improved performance, particularly with many clients and limited sample sizes.
Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered. However, in practice, data may be distributed across different parties (e.g., companies, devices) who intend to collectively learn a Bayesian network, but are not willing to disclose information related to their data owing to privacy or security concerns. In this work, we present a federated learning approach to estimate the structure of Bayesian network from data that is horizontally partitioned across different parties. We develop a distributed structure learning method based on continuous optimization, using the alternating direction method of multipliers (ADMM), such that only the model parameters have to be exchanged during the optimization process. We demonstrate the flexibility of our approach by adopting it for both linear and nonlinear cases. Experimental results on synthetic and real datasets show that it achieves an improved performance over the other methods, especially when there is a relatively large number of clients and each has a limited sample size.