FedGES让多方协作学贝叶斯网络，只传结构不传数据，保护隐私。
FedGES: A Federated Learning Approach for BN Structure Learning
- 用联邦学习+贪心等价搜索，只交换网络结构而非数据或参数。
- 在高维稀疏数据下仍能准确学习贝叶斯网络结构，收敛稳定。
- 适合医疗、金融等数据敏感领域的分布式建模，隐私友好。
贝叶斯网络（BN）结构学习传统上依赖集中式数据，当数据分布在多个实体时会引发隐私问题。本文提出针对去中心化场景的联邦学习方法 FedGES，基于贪心等价搜索（GES）算法实现 BN 结构学习。该方法通过仅交换不断演化的网络结构，而非参数或原始数据，有效解决隐私与安全挑战。通过结构融合机制，在多轮迭代中整合各客户端生成的有限模型，并引入受控结构融合策略，提升添加边时的客户端共识能力。在 { f bnlearn} 的 BN 数据库中的多种贝叶斯网络上进行实验，结果验证了 FedGES 的有效性，尤其在高维（变量数量大）和稀疏数据条件下表现优异，为现实世界中贝叶斯网络结构学习提供了可落地且隐私保护的解决方案。
原文摘要 · Abstract (English)
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.
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