联邦后勤回归与增强的隐私:通过ADMM从信息理论的角度来看,通过ADMM进行动态高斯扰动方法
Entropy (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了一种分布式逻辑回归高斯扰动 (DLGP) 算法,以提高联合学习中的隐私. 在分布式训练期间,DLGP使用差异隐私和ADMM来保护模型参数.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 网络安全 网络安全
背景情况:
- 联合学习在不分享原始信息的情况下,在分散的数据上训练模型.
- 在联合学习中传输模型参数存在重大隐私风险.
- 现有的方法很难有效地平衡隐私和模型实用性.
研究的目的:
- 提出一种新的算法,分布式逻辑回归高斯扰动 (DLGP),用于保护隐私的联合学习.
- 在分布式培训中解决模型参数传输的漏洞.
- 优化数据隐私和模型性能之间的权衡.
主要方法:
- 将乘数器 (ADMM) 的交替方向方法与校准差异隐私机制集成.
- 将集中后勤回归问题分解为在边缘节点上解决的独立的本地子问题.
- 将高斯式噪声注入到模型参数中,以保护隐私,并获得l2敏感度和 (ε,δ) 差异性隐私保证.
主要成果:
- DLGP算法有效地扰乱了与中央服务器共享的模型参数,从而增强了隐私.
- 对现实数据集的评估表明,在各种隐私预算中表现良好.
- 该方法在不同的节点数量和惩罚参数下显示了稳定性.
结论:
- 通过确保模型参数传输,DLGP为保护隐私的联合学习提供了可行的解决方案.
- 该算法成功地平衡了隐私要求与需要准确的模型训练的需求.
- DLGP 提供了严格的隐私保证,同时在分布式机器学习场景中保持了实际实用性.
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