Research On Incentive Mechanism For Privacy Protection In Federated Learnin

Federated learning,as a special distributed machine learning paradigm,has recently received widespread attention.However,since federated learning is a passive form of machine learning,it is essential to ensure user participation.In this paper,we investigate and analyze the incentive mechanism for privacy protection in federated learning,discussing how to select and incentivize users in federated learning with differential privacy;analyzing the impact of negotiation after user selection in federated learning;and addressing the issue of unfairness in incentive mechanisms.The specific research content includes the following:(1)By transforming federated learning into a classical economic model-reverse auction to achieve user selection.However,due to the fact that the user utility in federated learning with privacy protection is simultaneously affected by privacy protection strength and data volume,we convert reverse auction into a multi-attribute reverse auction.By establishing an optimization equation,we can achieve user selection and propose the FLRNDP-A algorithm.Experiments show that FLRNDP-A has higher accuracy than existing algorithms.(2)For users selected by the FLRNDP-A algorithm,we propose a post-auction negotiation theory,using the Rubinstein bargaining model to encourage users to reduce their privacy protection strength.This increases the benefits from the server’s perspective,and the theory demonstrates that post-auction negotiation improves social welfare.(3)In addition,to address the fairness issue in federated learning,we propose a Federated Learning Fair Incentive Mechanism(Fed FIM)to resolve the unfairness and "free-riding" behavior issues in existing monetary incentive mechanisms.This mechanism achieves aggregation fairness and reward fairness through efficient gradient aggregation and contribution assessment based on Shapley value.Experiments show that,compared to the FLRNDP-A algorithm,Fed FIM achieves higher accuracy with the same loss.This research aims to provide a comprehensive solution to the incentive and fairness issues in federated learning with privacy protection,offering valuable insights for further development and application of federated learning models in various scenarios.