信息网络安全 ›› 2024, Vol. 24 ›› Issue (1): 1-13.doi: 10.3969/j.issn.1671-1122.2024.01.001
收稿日期:
2023-08-26
出版日期:
2024-01-10
发布日期:
2024-01-24
通讯作者:
董琳
E-mail:donglin@cert.org.cn
作者简介:
吴昊天(1980—),男,江苏,副教授,博士,CCF会员,主要研究方向为可逆信息隐藏、隐私保护、口令猜解和区块链|李一凡(1984—),男,湖南,硕士研究生,主要研究方向为区块链、零知识证明|崔鸿雁(1978—)男,陕西,讲师,硕士,主要方向为信息安全、大数据应用|董琳(1985—),女,山西,高级工程师,博士,主要研究方向为互联网金融安全、网络信息安全
基金资助:
WU Haotian1, LI Yifan1, CUI Hongyan2, DONG Lin3()
Received:
2023-08-26
Online:
2024-01-10
Published:
2024-01-24
Contact:
DONG Lin
E-mail:donglin@cert.org.cn
摘要:
在跨孤岛联邦学习中,各参与者对最终训练出的模型贡献各异,如何评估他们的贡献并给予适当激励,成为联邦学习研究中一项关键问题。目前的激励方法主要着眼于奖励有效模型更新的参与者,同时惩罚不诚实者,侧重于激励计算行为。然而,参与者所提供的数据质量同样影响学习效果,但现有方法未充分考虑数据质量,并缺乏鉴定数据真实性的手段。为提升激励的准确性,需对参与者数据质量进行评估。通过融合零知识证明与区块链技术,文章提出一种评估参与者数据质量的协议,构建了全新联邦学习激励方案。该方案可在不泄露明文数据的前提下,评估参与者所用数据集质量,通过区块链系统向合格参与者发放激励,拒绝不合格者。实验证实,在部分用户提供虚假数据的情况下,该方案仍能准确给出激励结果,同时提升联邦学习模型的准确率。
中图分类号:
吴昊天, 李一凡, 崔鸿雁, 董琳. 基于零知识证明和区块链的联邦学习激励方案[J]. 信息网络安全, 2024, 24(1): 1-13.
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.
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