论文标题
在联邦环境中迈向隐私感知的因果结构学习
Towards Privacy-Aware Causal Structure Learning in Federated Setting
论文作者
论文摘要
因果结构学习已被广泛研究并广泛用于机器学习和各种应用中。为了实现理想的性能，现有的因果结构学习算法通常需要从多个数据源集中大量数据。但是，在隐私保护设置中，不可能将所有来源的数据集中并将其作为一个数据集集中在一起。为了保护数据隐私，近年来，联邦学习作为一种新的学习范式吸引了机器学习的很多关注。在本文中，我们研究了联合环境中的隐私感知因果结构学习问题，并提出了一种新型联合PC（FEDPC）算法，并采用两种新策略来保存数据隐私而不集中数据。具体而言，我们首先提出了一种新颖的层面聚合策略，以使PC算法无缝适应联合骨架学习的联合学习范式，然后我们设计了一种有效的策略，以学习一致的联合边缘方向的学习一致的分离集。广泛的实验验证了FEDPC在联邦学习环境中有效的因果结构学习有效。
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.
