Federated Incomplete Tabular Data Prediction with Missing Complementarity
Summary: DARN performs federated prediction on incomplete tables without imputation, using missing-aware attention and local missing-distribution modeling. Personalized aggregation exploits cross-client missing complementarity and sample size, yielding 25.8% gains under heterogeneous missingness. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yan Zhang (Zhejiang University)
- 2. Shuwei Liang (Zhejiang University)
- 3. Xiaoye Miao (Zhejiang University)
- 4. Yangyang Wu (Zhejiang University)
- 5. Jianwei Yin (Zhejiang University)
BibTeX Citation
@article{zhang_vldb25,
title = {{Federated Incomplete Tabular Data Prediction with Missing Complementarity}},
author = {Zhang, Yan and Liang, Shuwei and Miao, Xiaoye and Wu, Yangyang and Yin, Jianwei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3531--3544},
doi = {10.14778/3748191.3748213},
url = {https://doi.org/10.14778/3748191.3748213},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00011495357 |
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| 3,580 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.2888516e-05 |
| 5,645 | OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | 2023 | VLDB | 6.1366186e-05 |
| 6,421 | Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System | 2023 | VLDB | 5.8819368e-05 |
| 8,257 | FS-REAL: A Real-World Cross-Device Federated Learning Platform | 2023 | VLDB | 5.4574671e-05 |
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