A Gamified Research Tool for Incentive Mechanism Design in Federated Learning.

Federated Learning (FL) enables multiple participants to collaboratively train AI models in a privacy-preserving manner, which incurs cost during the training processing. This can be a significant issue especially for business participants [8]. These costs include communication, technical, compliance, risk of market share erosion and free-riding problems (i.e., participants may only join FL training with low-quality data) [6]. Motivating participants to contribute high-quality data continuously and maintain a healthy FL ecosystem is a challenging problem. The key to achieving this goal is through effective and fair incentive schemes. When designing such schemes, it is important for researchers to understand how FL participants react under different schemes and situations.