Federated Learning Incentive Scheme Based on Zero-Knowledge Proofs and Blockchain
In cross-silo federated learning,participants contribute differently to the final trained model.Evaluating their contributions and providing appropriate incentives has become a key issue in federated learning research.Current incentive methods primarily focus on rewarding participants who provide valid model updates while penalizing dishonest ones,emphasizing incentivizing computational behavior.However,the quality of data provided by participants also affects learning outcomes,yet existing methods inadequately consider data quality and lack means to verify data authenticity.To enhance incentive accuracy,it is necessary to evaluate the quality of participants'data.This paper introduced,for the first time,a protocol for assessing the quality of participants'data by integrating zero-knowledge proofs and blockchain technology,leading to a novel federated learning incentive scheme.This scheme can assess the quality of participants'datasets without disclosing plaintext data,utilizing blockchain systems to provide incentives to eligible participants while excluding those who don't meet the criteria.Experimental results confirm that even in scenarios where some users provide falsified data,this scheme remains capable of delivering accurate incentive results,while simultaneously improving the accuracy of the federated learning model.
万方数据
维普
