Enabling SQL-based Training Data Debugging for Federated Learning
Summary: Extends Rain-based SQL-based debugging to federated learning (FedRain) to prune mislabeled data causing unexpected model behavior. Then Frog refines the protocol for federated debugging, delivering security, accuracy, and efficiency over FedRain. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yejia Liu (Simon Fraser University)
- 2. Weiyuan Wu (Simon Fraser University)
- 3. Lampros Flokas (Columbia University)
- 4. Jiannan Wang (Simon Fraser University)
- 5. Eugene Wu (Columbia University)
BibTeX Citation
@article{liu_vldb22,
title = {{Enabling SQL-based Training Data Debugging for Federated Learning}},
author = {Liu, Yejia and Wu, Weiyuan and Flokas, Lampros and Wang, Jiannan and Wu, Eugene},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {3},
pages = {388--400},
doi = {10.14778/3494124.3494125},
url = {https://doi.org/10.14778/3494124.3494125},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,421 | Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System | 2023 | VLDB | 5.8819368e-05 |
| 7,911 | Incentive-Aware Decentralized Data Collaboration | 2023 | SIGMOD | 5.5181056e-05 |
| 8,456 | Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs | 2024 | VLDB | 5.4217837e-05 |
| 9,473 | FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data | 2023 | SIGMOD | 5.2634238e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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