Federated Incomplete Tabular Data Prediction with Missing Complementarity
Summary: DARN: a federated prediction framework for incomplete tabular data that directly leverages missing-complementarity across clients to optimize models without imputing missing values. Uses a missing-aware transformer (novel missing-attention) and personalized aggregation (complementarity+sample-size), yielding large SOTA gains. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yan Zhang
- 2. Shuwei Liang
- 3. Xiaoye Miao
- 4. Yangyang Wu
- 5. Jianwei Yin
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,143 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00013710269 |
| 1,895 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 0.00010180896 |
| 3,396 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1455126e-05 |
| 5,507 | OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | 2023 | VLDB | 5.4724291e-05 |
| 6,502 | Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System | 2023 | VLDB | 5.0361846e-05 |
| 7,937 | FS-REAL: A Real-World Cross-Device Federated Learning Platform | 2023 | VLDB | 4.613363e-05 |
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