{"type":"ScholarlyArticle","title":"Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection","authors":[{"name":"Xianjie Guo","slug":"xianjie-guo","given":"Xianjie","family":"Guo","affiliation":"School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Kui Yu","slug":"kui-yu","given":"Kui","family":"Yu","affiliation":"School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":"School of Software , Shandong University , China"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Xiaoxiao Li","slug":"xiaoxiao-li","given":"Xiaoxiao","family":"Li","affiliation":"Department of Electrical and Computer Engineering , The University of British Columbia , Canada"}],"abstract":"Federated causal discovery (FCD) aims to uncover causal relationships among variables from decentralized data across multiple clients, while preserving data privacy. In practice, the sample quality of each client's local data may vary across different variable spaces, referred to as sample quality heterogeneity. Thus, data from different clients might be suitable for learning different causal relationships among variables. Model aggregated under existing FCD methods requires the entire model parameters from each client, thereby being unable to handle the sample quality heterogeneity issue. In this paper, we propose the Federated Adaptive Causal Discovery (FedACD) method to bridge this gap. During federated model aggregation, it adaptively selects the causal relationships learned under the \"good\" variable space (i.e., one with high-quality samples) from each client, while masking those learned under the \"bad\" variable space (i.e., one with low-quality samples). This way, each client only needs to send the optimal learning results to the server, achieving accurate FCD. Extensive experiments on various types of datasets demonstrate significant advantages of FedACD over existing methods. The source code is available at https://github.com/Xianjie-Guo/FedACD.","doi":"10.24963/ijcai.2024/450","url":"https://ijcai.org/proceedings/2024/450","pdf":"https://ijcai.org/proceedings/2024/0450.pdf","markdown":"https://ijcai.org/proceedings/2024/450.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/450","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":450,"pages":{"first":4071,"last":4079},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Causality","Knowledge Representation and Reasoning: KRR: Causality","Uncertainty in AI: UAI: Causality, structural causal models and causal inference"],"videos":[],"references":[{"position":1,"raw":"[Abyaneh et al., 2022] Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, and Arash Mehrjou. 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