Efficient Core-Selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants collaboratively train a global model, and after the training is completed, each participant receives that global model along with an incentive. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their contributions. Furthermore, federated learning benefits from the cooperation of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes cooperative contributions has become an important issue to consider. In this article, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this issue, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. Meanwhile, to reduce the computational complexity of the core-selecting mechanism, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments demonstrate that the efficient core-selecting mechanism can incentivize truthful input of high-quality data and promote cooperation effectively, while it reduces computational overhead compared to the core-selecting mechanism.