Incentivizing Time-Aware Fairness in Data Sharing
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912664721031168 |
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| author | Chen, Jiangwei Pham, Kieu Thao Nguyen Sim, Rachael Hwee Ling Verma, Arun Wu, Zhaoxuan Foo, Chuan-Sheng Low, Bryan Kian Hsiang |
| author_facet | Chen, Jiangwei Pham, Kieu Thao Nguyen Sim, Rachael Hwee Ling Verma, Arun Wu, Zhaoxuan Foo, Chuan-Sheng Low, Bryan Kian Hsiang |
| contents | In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the parties incur data collection costs, they are only willing to do so when guaranteed incentives, such as fairness and individual rationality. Existing frameworks assume that all parties join the collaboration simultaneously, which does not hold in many real-world scenarios. Due to the long processing time for data cleaning, difficulty in overcoming legal barriers, or unawareness, the parties may join the collaboration at different times. In this work, we propose the following perspective: As a party who joins earlier incurs higher risk and encourages the contribution from other wait-and-see parties, that party should receive a reward of higher value for sharing data earlier. To this end, we propose a fair and time-aware data sharing framework, including novel time-aware incentives. We develop new methods for deciding reward values to satisfy these incentives. We further illustrate how to generate model rewards that realize the reward values and empirically demonstrate the properties of our methods on synthetic and real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Incentivizing Time-Aware Fairness in Data Sharing Chen, Jiangwei Pham, Kieu Thao Nguyen Sim, Rachael Hwee Ling Verma, Arun Wu, Zhaoxuan Foo, Chuan-Sheng Low, Bryan Kian Hsiang Machine Learning Computer Science and Game Theory In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the parties incur data collection costs, they are only willing to do so when guaranteed incentives, such as fairness and individual rationality. Existing frameworks assume that all parties join the collaboration simultaneously, which does not hold in many real-world scenarios. Due to the long processing time for data cleaning, difficulty in overcoming legal barriers, or unawareness, the parties may join the collaboration at different times. In this work, we propose the following perspective: As a party who joins earlier incurs higher risk and encourages the contribution from other wait-and-see parties, that party should receive a reward of higher value for sharing data earlier. To this end, we propose a fair and time-aware data sharing framework, including novel time-aware incentives. We develop new methods for deciding reward values to satisfy these incentives. We further illustrate how to generate model rewards that realize the reward values and empirically demonstrate the properties of our methods on synthetic and real-world datasets. |
| title | Incentivizing Time-Aware Fairness in Data Sharing |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2510.09240 |