Incentive-Aware Decentralized Data Collaboration
Summary: IDEA enables incentive-aware decentralized federated learning for data collaboration without a server, using a customizable reward scheme and a MARL incentive mechanism. It proves a Nash equilibrium and shows gains over four baselines on five real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yatong Wang (University of Electronic Science and Technology of China)
- 2. Yuncheng Wu (National University of Singapore)
- 3. Xincheng Chen (National University of Singapore)
- 4. Gang Feng (University of Electronic Science and Technology of China)
- 5. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@inproceedings{wang_sigmod23,
title = {{Incentive-Aware Decentralized Data Collaboration}},
author = {Wang, Yatong and Wu, Yuncheng and Chen, Xincheng and Feng, Gang and Ooi, Beng Chin},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589303},
url = {https://dl.acm.org/doi/10.1145/3589303},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,456 | Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs | 2024 | VLDB | 5.4217837e-05 |
| 10,214 | CoShap: A Scalable Coalition Growth Approach to Shapley Value Approximation | 2026 | SIGMOD | 5.093636e-05 |
| 11,214 | Performance-Based Pricing for Federated Learning via Auction | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00011495357 |
| 1,959 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 9.4090198e-05 |
| 3,237 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD | 7.6089416e-05 |
| 5,131 | Enabling SQL-based Training Data Debugging for Federated Learning | 2022 | VLDB | 6.3537809e-05 |
| 6,406 | Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 2022 | VLDB | 5.885424e-05 |
| 6,707 | Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | 2021 | VLDB | 5.7965835e-05 |
| 13,421 | DyHealth: Making Neural Networks Dynamic for Effective Healthcare Analytics | 2022 | VLDB | - |
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