FedCrowdSensing: Incentive Mechanism for Crowdsensing Based on Reputation and Federated Learning

Jianquan Ouyang, Wenke Wang

Abstract: In recent years, crowds en sing has become a hot topic in contemporary research. However, the traditional crowd-sensing model has some issues, such as low-quality data uploaded by users, privacy and security issues, and a lack of incentive for user participation. To address these challenges, we propose a crowdsensing framework that combines blockchain and federated learning to build a decentralized security framework. Our framework enables each participant to upload model gradient data to the crowdsensing platform for aggregation while ensuring user privacy and security. And we proposed a model aggregation method based on reputation value. In addition, we also designed a reverse auction algorithm based on historical reputation to filter the set of candidates who want to participate in the task, to obtain a higher quality set of participants. Security analysis and experimental results show that this model guarantees data quality and data privacy, and enhances user participation motivation.

External IDs:dblp:conf/iscc/OuyangW23