Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913116612198400 |
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| author | Sim, Rachael Hwee Ling Fan, Jue Tian, Xiao Xu, Xinyi Jaillet, Patrick Low, Bryan Kian Hsiang |
| author_facet | Sim, Rachael Hwee Ling Fan, Jue Tian, Xiao Xu, Xinyi Jaillet, Patrick Low, Bryan Kian Hsiang |
| contents | Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods fairly reward each source based on its data submitted as is. However, as these methods do not verify nor incentivize data truthfulness, the sources can manipulate their data (e.g., by submitting duplicated or noisy data) to artificially increase their valuations and rewards or prevent others from benefiting. This paper presents the first mechanism that provably ensures (F) collaborative fairness and incentivizes (T) truthfulness at equilibrium for Bayesian models. Our mechanism combines semivalues (e.g., Shapley value), which ensure fairness, and a truthful data valuation function (DVF) based on a validation set that is unknown to the sources. As semivalues are influenced by others' data, we introduce an additional condition to prove that a source can maximize its expected data values in coalitions and semivalues by submitting a dataset that captures its true knowledge. Additionally, we discuss the implications and suitable relaxations of (F) and (T) when the mediator has a limited budget for rewards or lacks a validation set. Our theoretical findings are validated on synthetic and real-world datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_11889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning Sim, Rachael Hwee Ling Fan, Jue Tian, Xiao Xu, Xinyi Jaillet, Patrick Low, Bryan Kian Hsiang Machine Learning Artificial Intelligence Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods fairly reward each source based on its data submitted as is. However, as these methods do not verify nor incentivize data truthfulness, the sources can manipulate their data (e.g., by submitting duplicated or noisy data) to artificially increase their valuations and rewards or prevent others from benefiting. This paper presents the first mechanism that provably ensures (F) collaborative fairness and incentivizes (T) truthfulness at equilibrium for Bayesian models. Our mechanism combines semivalues (e.g., Shapley value), which ensure fairness, and a truthful data valuation function (DVF) based on a validation set that is unknown to the sources. As semivalues are influenced by others' data, we introduce an additional condition to prove that a source can maximize its expected data values in coalitions and semivalues by submitting a dataset that captures its true knowledge. Additionally, we discuss the implications and suitable relaxations of (F) and (T) when the mediator has a limited budget for rewards or lacks a validation set. Our theoretical findings are validated on synthetic and real-world datasets. |
| title | Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.11889 |