Performance-Based Pricing for Federated Learning via Auction
Summary: Designs performance-based auctions for federated learning: one data-rich seller, multiple buyers, a template for truthful mechanisms optimizing social welfare or seller profit. Analyzes how test-vs-production performance noise degrades welfare/profit and validates empirically. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Zitao Li (Alibaba)
- 2. Bolin Ding (Alibaba)
- 3. Liuyi Yao (Alibaba)
- 4. Yaliang Li (Alibaba)
- 5. Xiaokui Xiao (National University of Singapore)
- 6. Jingren Zhou (Alibaba)
BibTeX Citation
@article{li_vldb24,
title = {{Performance-Based Pricing for Federated Learning via Auction}},
author = {Li, Zitao and Ding, Bolin and Yao, Liuyi and Li, Yaliang and Xiao, Xiaokui and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {6},
pages = {1269--1282},
doi = {10.14778/3648160.3648169},
url = {https://doi.org/10.14778/3648160.3648169},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00011495357 |
| 1,488 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010612416 |
| 3,759 | Dealer: An End-to-End Model Marketplace with Differential Privacy | 2021 | VLDB | 7.14674e-05 |
| 7,911 | Incentive-Aware Decentralized Data Collaboration | 2023 | SIGMOD | 5.5181056e-05 |
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