通过区块链进行双边防御的联合学习
Jue Xiao1, Hewang Nie1, Zepu Yi1
1School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
概括
本研究介绍了FedBASS,这是一个新的联合学习方案,使用区块链和双服务器来防御隐私侵犯和毒害攻击. 联邦数据库 (FedBASS) 增强了模型的安全性和隐私,即使使用非IID数据和不可信赖的参与者.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 增强了数据隐私,但面临着数据泄露和中毒攻击等安全威胁.
- 现有的FL解决方案往往只解决一个安全问题,在全面的防御中留下了一个空白.
- 一个不完全可信的模型,有恶意客户端和诚实但好奇的服务器,提出了一个复杂的安全挑战.
研究的目的:
- 提出一个强大的联邦学习计划,FedBASS,它既解决了隐私侵犯,也解决了模型中毒攻击.
- 开发一个对非IID数据有弹性的系统,并对恶意客户端和服务器进行保护.
- 确保联合学习环境的透明度,稳定性和实用性.
主要方法:
- 实现了双服务器架构 (分析器和验证器) 与 CKKS 加密以实现梯度安全.
- 用于检测恶意客户端和非IID数据的梯度补偿策略.
- 整合了一个削弱的差异性隐私计划,在集群期间为隐私进行混合,并为通信完整性使用区块链.
主要成果:
- 在具有挑战性的威胁模型中,FedBASS有效地减轻了隐私侵犯和毒害攻击.
- 该方案通过动态聚类和梯度补偿证明了对非IID数据的稳定性.
- 区块链集成确保了透明度,并防止了自私的行为,提高了整体系统的完整性.
结论:
- 在联邦学习中,FedBASS成功地平衡了模型忠实性,稳定性,效率和隐私.
- 拟议的方案为复杂,不值得信赖的环境提供了一种实用且安全的解决方案.
- 这项工作通过同时解决多种威胁,促进了联邦学习安全.
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