Incentivizing Time-Aware Fairness in Data Sharing
This addresses fairness and incentive issues for parties in collaborative data sharing, particularly in scenarios with asynchronous participation, though it is incremental as it builds on existing frameworks by adding time-awareness.
The paper tackles the problem of incentivizing data sharing in collaborative machine learning when parties join at different times, proposing a time-aware fairness framework that rewards earlier contributors with higher value and demonstrates its properties on synthetic and real-world datasets.
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.