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Federated local causal structure learning
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DOI：10.1007/s11432-023-4203-6.png)
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Abstract
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Local causal structure learning (LCS) efficiently identifies a set of direct neighbors of a specified variable from observational data. Additionally, it distinguishes direct causes and direct effects of this variable without learning the entire causal structure. While many LCS algorithms have been proposed, they do not consider the data privacy-preserving problem, which has attracted extensive attention from academia and industry. To address this issue, we propose a federated local causal structure learning (FedLCS) algorithm to learn local causal structures in privacy-preserving data in a federated setting. Specifically, FedLCS introduces a layer-wise federated local skeleton learning algorithm to construct the local skeleton. Based on this skeleton, it introduces a federated local skeleton orientation algorithm and an extension-and-backtracking orientation algorithm to orient the edges. Finally, FedLCS uses a federated local extension-and-backtracking orientation algorithm to orient the remaining edges. Extensive experiments on benchmark, synthetic, and real datasets demonstrate that FedLCS can learn the local causal structure of a given variable in a federated setting.
Keywords:
local causal structure learning
federated learning
directed acyclic graph
privacy-preserving data
federated layer-wise strategy
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