Towards Federated Bayesian Network Structure Learning with Continuous Optimization-猫眼课题宝

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2021-10

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Ignavier Ng;Kun Zhang

传统上，贝叶斯网络结构学习通常在一个中心站点进行，所有数据都收集在该中心站点。然而，在实践中，数据可以跨不同方分布（例如，公司、设备），他们打算共同学习贝叶斯网络，但由于隐私或安全考虑而不愿意公开与他们的数据相关的信息。在这项工作中，我们提出了一个联邦学习方法来估计贝叶斯网络的结构，从数据是水平划分在不同的缔约方。我们开发了一个分布式结构学习方法的基础上连续优化，使用交替方向的乘法器（ADMM），只有模型参数必须在优化过程中进行交换。我们证明了我们的方法通过采用它的线性和非线性的情况下的可扩展性。在合成数据集和真实的数据集上的实验结果表明，该方法比其他方法具有更好的性能，特别是在客户端数量相对较多且每个客户端的样本量有限的情况下。

Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered. However, in practice, data may be distributed across diﬀerent parties (e.g., companies, devices) who intend to collectively learn a Bayesian network, but are not willing to disclose information related to their data owing to privacy or security con-cerns. In this work, we present a federated learning approach to estimate the structure of Bayesian network from data that is horizontally partitioned across diﬀerent parties. We develop a distributed structure learning method based on continuous optimization, using the alternating direction method of multipliers (ADMM), such that only the model parameters have to be exchanged during the optimization process. We demonstrate the ﬂexibility of our approach by adopting it for both linear and nonlinear cases. Experimental results on synthetic and real datasets show that it achieves an improved performance over the other methods, especially when there is a relatively large number of clients and each has a limited sample size.

Towards Federated Bayesian Network Structure Learning with Continuous Optimization

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