瓦瑟斯坦基于距离的深度泄漏从梯度.
Zifan Wang1, Changgen Peng1,2, Xing He1,3
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
联合学习隐私得到了WDLG的增强,WDLG是一种改进梯度攻击的新方法. 这种瓦瑟斯坦距离方法提高了合速度和图像重建的准确性.
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:
- 联合学习 (FL) 通过共享平均梯度来保护数据隐私.
- 基于梯度的攻击,如从梯度的深度泄漏 (DLG),可以从共享梯度重建私人训练数据.
- 现有的DLG方法存在缓慢的收和糟糕的重建准确性.
研究的目的:
- 引入一种新的瓦斯斯坦基于远程DLG (WDLG) 方法,以加强联邦学习中的隐私保护.
- 为了提高基于梯度的隐私攻击的融合速度和准确性.
- 为了解决现有的DLG算法的局限性.
主要方法:
- 拟议的WDLG方法使用瓦瑟斯坦距离作为训练损失函数.
- 使用利普希茨条件和康托罗维奇-鲁宾斯坦二元性的瓦瑟斯坦距离的代计算.
- 理论分析证明了瓦瑟斯坦距离的可微分性和连续性.
主要成果:
- 在训练速度和反转图像质量方面,WDLG在DLG上表现优越.
- 实验验证证了WDLG在增强基于梯度的攻击重建方面的有效性.
- 差异性隐私被证明可以提供有效的干扰保护,防止WDLG.
结论:
- 在联合学习中,WDLG方法为基于梯度的隐私攻击提供了显著的改进.
- 瓦瑟斯坦距离是一种可行的和有效的损失函数,用于提高攻击精度和收.
- 不同隐私集成是强大的隐私保护深度学习框架的一个有希望的方向.
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