FedDAG: Federated DAG Structure Learning

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dc.contributor.authorGao, E.
dc.contributor.authorChen, J.
dc.contributor.authorShen, L.
dc.contributor.authorLiu, T.
dc.contributor.authorGong, M.
dc.contributor.authorBondell, H.
dc.date.issued2023
dc.descriptionReviewed on OpenReview: https: // openreview. net/ forum? id= MzWgBjZ6Le
dc.description.abstractTo date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: how do we discover the underlying DAG structure from decentralized data? In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
dc.description.statementofresponsibilityErdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell
dc.identifier.citationTransactions on Machine Learning Research, 2023; 2023-January:1-36
dc.identifier.issn2835-8856
dc.identifier.orcidGao, E. [0000-0003-1736-2764]
dc.identifier.urihttps://hdl.handle.net/2440/147155
dc.language.isoen
dc.publisherJournal of Machine Learning Research
dc.relation.granthttp://purl.org/au-research/grants/arc/DP180103424
dc.relation.granthttp://purl.org/au-research/grants/arc/DE190101473
dc.relation.granthttp://purl.org/au-research/grants/arc/IC190100031
dc.relation.granthttp://purl.org/au-research/grants/arc/DP220102121
dc.relation.granthttp://purl.org/au-research/grants/arc/FT220100318
dc.relation.granthttp://purl.org/au-research/grants/arc/DE210101624
dc.rightsOpen Access © CC BY 4.0
dc.source.urihttps://openreview.net/forum?id=MzWgBjZ6Le
dc.subjectcs.LG
dc.subjectstat.ML
dc.titleFedDAG: Federated DAG Structure Learning
dc.typeJournal article
pubs.publication-statusPublished

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