Dynamic Incentive Model for Federated Learning Model Trading via Evolutionary Game Theory

Tianxiang Chen, Feng Wang, Wenjie Hou, Shaoting Tang, Zhiming Zheng

Abstract: Federated Learning (FL) is an emerging decentralized machine learning paradigm that addresses the data-silo problem through privacy-preserving collaborative model training, attracting significant attention from academia and industry. However, model trading in FL involves multiple stakeholders, including data owners, model requesters, and the cloud service platform, whose conflicting interests hinder the sustainability and stability of FL. To address these challenges, this paper considers the bounded rationality of the three parties involved in long-term dynamic decision-making and constructs a tripartite evolutionary game model based on evolutionary game theory, further taking into account collusion between data owners and cloud service platforms. We analyze the evolutionary dynamics involved, theoretically revealing the social dilemma of dishonesty in FL model trading. To prevent dishonest behaviors such as free-riding and false reporting, we apply the replicator dynamics and Lyapunov method to analyze the impact of rewards, punishments, and collusion costs on the evolutionary stable strategies of the three parties and propose incentive strategies. Simulation experimental results validate that our incentive model is effective in alleviating dishonest social dilemmas and improving social welfare.