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相关概念视频

Poisson's And Laplace's Equation01:25

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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx  and a shunt capacitance CΔx.
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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
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通过反向影响函数理解深度梯度泄漏.

Haobo Zhang1, Junyuan Hong1,2, Yuyang Deng3

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概括
此摘要是机器生成的。

深度梯度泄漏 (DGL) 攻击从梯度中恢复私人训练图像. 我们引入了反向影响函数 (I2F),以了解和减轻分布式学习中的隐私风险.

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科学领域:

  • 人工智能的人工智能
  • 机器学习安全 机器学习安全
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 深度梯度泄漏 (DGL) 通过从共享梯度中恢复训练数据,在分布式学习中构成重大隐私风险.
  • 了解深度网络中隐私泄露的机制对于开发有效的防御至关重要,但由于它们的黑子性质,仍然具有挑战性.

研究的目的:

  • 开发一种新的方法,即反向影响函数 (I2F),用于分析深度学习中的隐私泄露.
  • 建立一个可扩展和高效的工具,以了解什么时候以及如何在基于梯度的攻击中发生隐私泄露.

主要方法:

  • 提出了反向影响函数 (I2F),它隐含地解决了深度梯度泄漏问题.
  • I2F只需要Oracle访问梯度和雅可比矢量产品,使其适用于深度网络.
  • 在不同的模型架构,数据集和防御中经验验证I2F对DGL的近似.

主要成果:

  • I2F有效地在各种设置中近似深度梯度泄漏,证明了其通用性.
  • 该研究提供了对隐私保护的最佳梯度扰动策略的见解.
  • 分析揭示了隐私保护中的不平等,并提出了隐私首选模型初始化方法.

结论:

  • 反向影响函数 (I2F) 是一种可扩展和有效的工具,用于分析深度学习中的隐私泄露.
  • I2F能够更深入地了解深度梯度泄漏,促进开发更强大的隐私保护技术.
  • 这项工作有助于在分布式机器学习系统中增强数据隐私.