基于秘密共享的集团可验证的安全聚合联合学习.
Sufang Zhou1,2, Lin Wang1, Liangyi Chen1
1School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
Scientific reports
|March 21, 2025
概括
本研究介绍了GVSA,这是一个高效的联合学习方案,使用秘密共享来增强隐私和弹性. GVSA最大限度地降低了计算开销,使得安全的机器学习在资源有限的环境中变得实用.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 解决了数据孤岛和原始数据安全问题,但面临隐私泄露和服务器改风险.
- 在FL中现有的隐私保护方法往往需要高额的计算和通信成本,这限制了它们在资源有限的环境中使用.
研究的目的:
- 提出一个高效和安全的聚合方案,GVSA (集成向量秘密聚合),用于联合学习.
- 加强隐私,对用户退出的弹性,以及联合学习系统的效率.
主要方法:
- GVSA采用一种掩盖技术,用于本地模式的隐私和秘密共享,以抵御用户退出.
- 该方案使用双聚合方法,使用轻量级验证标签进行准确性验证.
- 实施一个分组策略,以减少用户和服务器的计算负载.
主要成果:
- GVSA展示了高安全性和有效的模型准确性保护.
- 与FedAvg相比,GVSA仅显示了~7%的额外计算开销.
- 在相同的安全级别下,GVSA的训练速度比其他安全聚合方案快2.3倍.
结论:
- GVSA为联合学习提供了一种高效,安全的解决方案,特别适合资源有限的环境.
- 拟议的方案平衡了强有力的隐私保证与实际的性能改进.
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