A novel approach for fair incentive social physical data based on blockchain-federated learning

Introduction A key research focus in FL is the incentive mechanism. To ensure that all data owners actively contribute their data for model training, it is necessary to establish a fair incentive system that encourages them to share useful data. A well-functioning incentive system enables all participants to continuously and effectively train models, which in turn enhances the accuracy of the ultimately trained federated model. Methods This paper proposes a new algorithm for optimizing the incentive mechanism. Initially, clients who possess high-quality data can participate in the training due to their reputation value. The client entrusted local data training to the high-performance fog node by auctioning local training tasks to it. The aim of this action was to improve the efficiency of local training and tackle the problem of differing performance levels among clients. Finally, the global gradient aggregation algorithm removes malicious clients from the local gradient. Results and Discussion Results from the simulation demonstrate that the suggested algorithm outperforms current algorithms.