Federated Learning Incentive Scheme Based on Zero-Knowledge Proofs and Blockchain

doi:10.3969/j.issn.1671-1122.2024.01.001

Abstract

Abstract:

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.

Key words: zero-knowledge proofs, blockchain, incentive mechanism, federated learning, data quality assessment

CLC Number:

TP309

WU Haotian, LI Yifan, CUI Hongyan, DONG Lin. Federated Learning Incentive Scheme Based on Zero-Knowledge Proofs and Blockchain[J]. Netinfo Security, 2024, 24(1): 1-13.

Figures/Tables 11

References 46

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是否使用数据扩充	识别准确率
使用数据扩充	98.2%
不使用数据扩充	50.0%

有效数据数据集	识别准确率
F-MNIST	94.3%
K-MNIST	97.9%

分类器	100%有效数据		95%有效数据
数据集	平均值	标准差	平均值	标准差
MNINST	98.3%	0.61%	93.1%	0.63%
F-MNIST	94.1%	1.09%	89.4%	1.06%
K-MNIST	97.7%	0.65%	92.3%	0.67%

合约名称	执行成功	平均延迟/s	吞吐量/TPS
发起任务	5000	0.72	10.30
查询任务	5000	0.06	112.90
提交申请	5000	5.40	1.37
审核申请	5000	0.13	52.03