Federated causal structure learning with missing data

doi:10.1016/j.knosys.2025.114601

Federated causal structure learning with missing data

2025-10-10

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DOI：10.1016/j.knosys.2025.114601

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Abstract

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• A federated causal structure learning method applicable to missing data is proposed. • The strategy of handling missing data comes from the server perspective. • The method addresses the issues of the ignorance of client’s weights. • With varying missing rates and client’s numbers, the method has significant benefits.

Federated causal structure learning with missing data

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