Q-A DE2: A Novel Approach for Solving Federated Learning Incentive Mechanisms Based on Stackelberg Game

Federated learning (FL) is a framework for distributed privacy-preserving computation, which enables global model aggregation by receiving client-trained models without accessing clients’ raw data. Therefore, developing an efficient incentive mechanism to promote client participation within this secure framework is a primary challenge in this field. To address this, we consider an FL system with differential privacy (DP) and model the interactions between the server and clients as a two-stage Stackelberg game aimed at minimizing server operating costs and maximizing client rewards. Then, a quality-based adaptive differential evolution-2 algorithm (Q-ADE2) is proposed to approximately solve the game model, which involves optimizing the server’s reward allocation to clients and determining the clients’ privacy budgets. In this approach, the quality coefficient is determined by the effectiveness of the FL system, evaluated based on the improvement in model accuracy. Finally, the standard MNIST dataset is employed in three comparative experiments, demonstrating that the proposed algorithm exhibits strong solving performance and effectively incentivizes client participation in FL training.