在保护隐私的前提下，高效学习分布式数据中的贝叶斯网络结构。
Differentially Private and Federated Structure Learning in Bayesian Networks
- 结合差分隐私与贪心更新，仅传输少数关键边信息。
- 通信开销低，隐私预算利用充分，结构估计准确度接近非私有基线。
- 适合医疗、金融等需严防数据泄露的分布式建模场景。
从分布式数据中学习贝叶斯网络结构面临两大挑战：(i) 为参与者提供严格的隐私保障；(ii) 避免通信成本随维度增长而急剧上升。本文提出一种新型联邦方法 Fed-Sparse-BNSL，用于学习线性高斯贝叶斯网络结构，同时解决上述问题。通过将差分隐私与仅针对每位参与者的少数相关边进行贪心更新相结合，该方法高效利用隐私预算，同时保持低通信开销。精心设计的算法保证了模型可识别性，支持高精度结构推断。在合成与真实数据集上的实验表明，Fed-Sparse-BNSL 在保持接近非私有基线的性能的同时，显著提升了隐私保护水平和通信效率。
原文摘要 · Abstract (English)
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.
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