FedGES: A Federated Learning Approach for BN Structure Learning
This provides a privacy-preserving solution for entities with decentralized data in BN structure learning, though it is incremental as it adapts an existing algorithm to a federated setting.
The paper tackles the problem of privacy concerns in Bayesian Network structure learning with distributed data by introducing FedGES, a federated learning approach that exchanges only network structures, achieving effective results in high-dimensional and sparse scenarios as validated on bnlearn's BN Repository.
Bayesian Network (BN) structure learning traditionally centralizes data, raising privacy concerns when data is distributed across multiple entities. This research introduces Federated GES (FedGES), a novel Federated Learning approach tailored for BN structure learning in decentralized settings using the Greedy Equivalence Search (GES) algorithm. FedGES uniquely addresses privacy and security challenges by exchanging only evolving network structures, not parameters or data. It realizes collaborative model development, using structural fusion to combine the limited models generated by each client in successive iterations. A controlled structural fusion is also proposed to enhance client consensus when adding any edge. Experimental results on various BNs from {\sf bnlearn}'s BN Repository validate the effectiveness of FedGES, particularly in high-dimensional (a large number of variables) and sparse data scenarios, offering a practical and privacy-preserving solution for real-world BN structure learning.