Yardstick-Stackelberg pricing-based incentive mechanism for Federated Learning in Edge Computing

Abstract

Federated Learning (FL) enables collaborative model training across multiple participants without sharing original data, making it a valuable tool for preserving privacy in Mobile Edge Computing (MEC) environment. However, due to users’ varying levels of motivation and commitment, it is challenging to incentivize effective participation in FL. To address this, we propose a pricing-based incentive mechanism that enhances FL efficiency and energy sustainability in MEC. To be specific, we firstly develop the formula of incentive mechanism based on the yardstick pricing rule. Subsequently, we determine the optimal hyperparameters of the utility function aiming to maximize model accuracy. Additionally, we formulate a Stackelberg game to derive optimal reward strategies, balancing users’ transmission power allocation and the server’s reward distribution. Simulation results show that our proposed scheme outperforms other existing schemes with over 98.2% accuracy, 0.7% server utility enhancement, and 14.6% server loss decrease compared with static incentives. Moreover, our proposed scheme contributes to faster growth in both server and users utilities when compared with the advanced schemes by varying user numbers, which demonstrates its better scalability and adaptability.