Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation
Summary: Unified empirical evaluation of federated-learning contribution estimation methods across effectiveness (coalition-aware utility), robustness to attacks (replication, label-flip), and computational cost. Surveys prior methods, reveals trade-offs, and releases an adaptable testing framework to guide future design. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yiwei Chen (Tsinghua University)
- 2. Kaiyu Li (Tsinghua University)
- 3. Guoliang Li (Tsinghua University; Zhongguancun Lab)
- 4. Yong Wang (Tsinghua University)
BibTeX Citation
@article{chen_vldb24,
title = {{Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation}},
author = {Chen, Yiwei and Li, Kaiyu and Li, Guoliang and Wang, Yong},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {2077--2090},
doi = {10.14778/3659437.3659459},
url = {https://doi.org/10.14778/3659437.3659459},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,541 | Shapley Value Estimation Based on Differential Matrix | 2025 | SIGMOD | 5.2528121e-05 |
| 10,198 | ASSS: Adaptive Stratified Sampling for Shapley-like Values | 2026 | SIGMOD | 5.093636e-05 |
| 10,934 | PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 281 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022445849 |
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 2,025 | Data Market Platforms: Trading Data Assets to Solve Data Problems | 2020 | VLDB | 9.2903125e-05 |
| 4,106 | Secure Shapley Value for Cross-Silo Federated Learning | 2023 | VLDB | 6.8980966e-05 |
| 6,145 | Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets | 2023 | VLDB | 5.9604471e-05 |
| 6,272 | On Shapley Value in Data Assemblage Under Independent Utility | 2022 | VLDB | 5.9319813e-05 |
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