Reliable incentive mechanism in hierarchical federated learning based on two-way reputation and contract theory

Abstract

Hierarchical federated learning (HFL) can effectively alleviate the communication bottleneck of traditional federated learning. However, the long-term healthy development of federated learning needs to continue to attract reliable participants with high-quality data. Therefore, we propose a novel reliable incentive mechanism based on two-way reputation and contract theory. Firstly, a two-way reputation mechanism named TWRM is introduced to evaluate the reliability of cluster heads and mobile devices simultaneously. Secondly, a reputation blockchain is designed to prevent malicious nodes from tampering with reputation scores, and effectively deal with malicious edge servers colluding with mobile devices. Finally, contract theory is used to reward customers based on data quality and encourage them to provide high-quality training data. Experimental results can verify that the scheme proposed in this paper effectively ensures the reliability and data quality of the participants, which can help to attract more reliable participants with high-quality data to join in federated learning.