Research On Incentive Mechanism And Privacy Protection Scheme Based On Federated Learnin

With the rapid growth of data and the need for privacy protection,Federated Learning,a new data-sharing paradigm that enables data to be kept local and effectively breaks down data silos,has become an effective means of solving the problems of largescale data analysis and privacy protection.However,federated learning still faces the following two problems in its practical application: One is the lack of incentive.With users keeping personal data locally and the need to consume computational and communication resources to train models,the lack of incentives for users to engage in federated learning limits the development of federated learning.Another is the issue of privacy leakage.Although federated learning enables data to stay local,in the process of uploading model parameters to the server for aggregation after local model training,adversaries can still access the parameter information to reconstruct the data,and the privacy protection advantage on which federated learning relies faces a great challenge.Therefore,the goal of this paper is to investigate a federated learning-based incentive and privacy protection scheme to improve user motivation and protect data privacy,with the following work:(1)A federated learning incentive mechanism based on the Starkelberg game is proposed.Firstly,the Top-K cost selection algorithm is designed based on reverse auction,thus reducing the cost for task publishers to select data owners.Secondly,the multi-factor reward function is designed based on reputation,accuracy,and reward rate,and data owners with high reputation and accuracy will receive more rewards.Finally,a two-stage Stackelberg game model is constructed for task publishers and data owners,and the two are derived from Nash equilibrium solutions for each other.(2)A privacy-preserving scheme for federated learning based on generative adversarial networks is proposed.Firstly,the shadow data is generated by generating adversarial networks to learn the distribution features of the original data.Secondly,the original model is hidden and the shadow model is trained to replace the original model with the shadow data.Finally,the original gradient was replaced by the shadow gradient generated by the shadow data in the shadow model and was not accessible to the adversary.