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安全的等级异步联合学习与混动模型和Mask-DP
Yonghui Chen1,2,3, Daxiang Ai1, Linglong Yan1
1School of Computer Science, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
安全的等级异步联合学习 (SHAFL) 增强了联合学习的隐私和稳定性. 这个框架可以防止推断和勾结攻击,改善全球模型性能和安全性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 层次异步联合学习 (HAFL) 提供了实用的沟通和聚合,但在隐私和稳定性方面存在困难.
- 在HAFL中的恶意节点可以损害数据隐私和毒害全球模型,降低系统完整性.
研究的目的:
- 提出一个安全的等级异步联合学习 (SHAFL) 框架,以应对HAFL的隐私和稳定性挑战.
- 加强联合学习系统的安全性和性能,防止恶意攻击和数据推断.
主要方法:
- SHAFL将节点组织成具有网关的组,使用面具差异隐私 (DP) 交换和同态加密 (HE) 来防止推断和勾结攻击.
- SHAFL采用可消除的噪音,混合模型和分样,以改善本地模型的隐私并减少噪音对全球性能的影响.
- 在SHAFL中的全球模型聚合平衡了准确性和通信延迟,减轻了恶意和陈旧模型的影响.
主要成果:
- 与现有解决方案相比,SHAFL展示了优越的融合,安全性,稳定性和隐私保护能力.
- 该框架有效地防止了来自网关和委员会节点的推断攻击以及培训节点之间的勾结攻击.
- SHAFL减少了噪声对性能的影响,并减轻了与恶意或延迟模型更新相关的风险.
结论:
- 拟议的SHAFL框架显著提高了分层异步联合学习的安全性,稳定性和隐私性.
- 对于需要强大的数据保护和系统完整性的真实世界联合学习应用程序,SHAFL提供了一个实用的解决方案.
- SHAFL代表了安全和强大的联合学习系统的最新进展.
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