Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attracted much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in a federated learning setting.

翻译：因果结构学习已被广泛研究并应用于机器学习及各类场景中。为实现理想性能，现有因果结构学习算法通常需要将来自多个数据源的大量数据集中处理。然而，在隐私保护场景下，无法将所有数据源的数据集中整合为单一数据集。为保护数据隐私，联邦学习作为一种新型学习范式近年来在机器学习领域备受关注。本文研究了联邦场景中的隐私感知因果结构学习问题，提出了一种新颖的联邦PC算法（FedPC），该算法采用两种新策略在不集中数据的前提下保护数据隐私。具体而言，我们首先提出了一种新颖的逐层聚合策略，使PC算法能够无缝适配联邦学习范式以完成联邦骨架学习；随后设计了一种有效策略，用于学习联邦边定向过程中的一致分离集。大量实验证明，FedPC在联邦学习场景下能够有效进行因果结构学习。