Federated learning is a distributed machine learning system that uses
participants' data to train an improved global model. In federated learning,
participants cooperatively train a global model, and they will receive the
global model and payments. 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 data
quality. Furthermore, federated learning benefits from the cooperative
contributions of participants. Accordingly, how to establish an incentive
mechanism that both incentivizes inputting data truthfully and promotes stable
cooperation has become an important issue to consider. In this paper, 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, our core-selecting mechanism employs a
relaxation method and simultaneously minimizes the benefits of inputting false
data for all participants. However, this mechanism is computationally expensive
because it requires aggregating exponential models for all possible coalitions,
which is infeasible in federated learning. To address this, 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 verify that the efficient core-selecting mechanism can
incentivize inputting high-quality data and stable cooperation, while it
reduces computational overhead compared to the core-selecting mechanism.

通过引入数据共享博弈模型和博弈论方法，设计了一种基于核心选择的激励机制，在减少计算开销的同时，激励参与者输入高质量数据和保持稳定合作关系，验证了其有效性。