Federated Learning of Dynamic Bayesian Network via Continuous Optimization From Time Series Data

Summary

This paper introduces a federated learning approach for estimating the structure of a dynamic Bayesian network from time series data split horizontally across parties. It extends the method to heterogeneous data using a proximal operator as a regularization term in a personalized framework, and proves convergence to a stationary point. Experiments on synthetic and real-world datasets show it outperforms state-of-the-art techniques, especially with many clients and small per-client samples.