Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection
Authors: Xianjie Guo, Kui Yu, Hao Wang, Lizhen Cui, Han Yu, Xiaoxiao Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various types of datasets demonstrate significant advantages of Fed ACD over existing methods. The source code is available at https://github.com/Xianjie-Guo/Fed ACD. |
| Researcher Affiliation | Academia | Xianjie Guo1 , Kui Yu1 , Hao Wang1 , Lizhen Cui2 , Han Yu3 and Xiaoxiao Li4 1School of Computer Science and Information Engineering, Hefei University of Technology, China 2School of Software, Shandong University, China 3College of Computing and Data Science, Nanyang Technological University, Singapore 4Department of Electrical and Computer Engineering, The University of British Columbia, Canada |
| Pseudocode | Yes | Due to space limit, the detailed pseudo-code of Fed ACD is provided in Appendix B, and its time complexity is analyzed in Appendix C. |
| Open Source Code | Yes | The source code is available at https://github.com/Xianjie-Guo/Fed ACD. |
| Open Datasets | Yes | Benchmark BN datasets. We use three benchmark BN datasets: Child with 20 variables, Insurance with 27 variables and Alarm with 37 variables, and each dataset contains 5,000 samples [Tsamardinos et al., 2006]. and Synthetic Non-IID datasets. We employ the publicly available code from [Gao et al., 2023] to generate two batches of Non-IID datasets and Real-world datasets. We also compare the proposed method with the baselines on two non-overlapping networks of sizes {8, 20} from the lung cancer geneexpression dataset, REGED [Statnikov et al., 2015]. |
| Dataset Splits | No | The paper mentions training and testing, but it does not explicitly define or specify a separate validation dataset split with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud instance types, used for running the experiments. |
| Software Dependencies | No | The paper mentions using publicly available code from other works and refers to an Appendix for implementation details, but it does not explicitly list specific software dependencies with their version numbers (e.g., Python 3.x, PyTorch x.x). |
| Experiment Setup | No | The paper states 'Implementation details of the Fed ACD algorithm and the baselines are provided in Appendix F', but this appendix is not included in the provided text. The main body of the paper does not specify hyperparameters or other concrete experimental setup details. |