Federated Dynamic Bayesian Network Structure Learning with Continuous Optimization

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Traditionally, learning the structure of a Dynamic Bayesian network has been centralized, with all data pooled in one location. However, in real-world scenarios, data is often dispersed among multiple parties (e.g., companies, devices) that aim to collaboratively learn a Dynamic Bayesian network while preserving their data privacy and security. In this study, we introduce a federated learning approach for estimating the structure of a Dynamic Bayesian network from data distributed horizontally across different parties. We propose a distributed structure learning method that leverages continuous optimization so that only model parameters are exchanged during optimization. Experimental results on synthetic and real datasets reveal that our method outperforms other techniques, particularly when there are many clients with limited individual sample sizes.