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
- 2. Bolin Ding
- 3. Liuyi Yao
- 4. Yaliang Li
- 5. Xiaokui Xiao
- 6. Jingren Zhou
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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 |
|---|---|---|---|---|
| 1,143 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00013710269 |
| 1,298 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012758104 |
| 1,891 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010194092 |
| 3,836 | Dealer: An End-to-End Model Marketplace with Differential Privacy | 2021 | VLDB | 6.7153977e-05 |
| 7,487 | Incentive-Aware Decentralized Data Collaboration | 2023 | SIGMOD | 4.7180617e-05 |
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