Differentially Private and Federated Structure Learning in Bayesian Networks
This addresses privacy and efficiency challenges for participants in federated learning of Bayesian networks, though it appears incremental as it builds on existing federated and private learning techniques.
The paper tackled the problem of learning Bayesian network structures from decentralized data by introducing Fed-Sparse-BNSL, a method that combines differential privacy with greedy updates to ensure privacy and low communication costs, achieving utility close to non-private baselines in experiments.
Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for participants, and (ii) avoiding communication costs that scale poorly with dimensionality. In this work, we introduce Fed-Sparse-BNSL, a novel federated method for learning linear Gaussian Bayesian network structures that addresses both challenges. By combining differential privacy with greedy updates that target only a few relevant edges per participant, Fed-Sparse-BNSL efficiently uses the privacy budget while keeping communication costs low. Our careful algorithmic design preserves model identifiability and enables accurate structure estimation. Experiments on synthetic and real datasets demonstrate that Fed-Sparse-BNSL achieves utility close to non-private baselines while offering substantially stronger privacy and communication efficiency.