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相关概念视频

Gradient and Del Operator01:14

Gradient and Del Operator

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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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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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相关实验视频

Updated: May 3, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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从梯度进行生成图像重建.

Ekanut Sotthiwat, Liangli Zhen, Chi Zhang

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    此摘要是机器生成的。

    从梯度生成图像重建 (GIRG) 可以从联合学习 (FL) 中的共享梯度中恢复高分辨率的训练图像. 这种方法在没有先前数据知识的情况下重建图像,突出了FL的隐私漏洞.

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    相关实验视频

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    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 网络安全 网络安全

    背景情况:

    • 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模式培训,从而保护客户的隐私.
    • 以前的研究表明,FL共享的梯度可以被利用来推断私人训练数据,包括图像重建.
    • 现有的梯度逆转方法在图像分辨率,批量大小方面存在局限性,并且通常需要对客户的数据集的预先了解.

    研究的目的:

    • 提出一种新的方法,即从梯度中生成图像重建 (GIRG),用于从FL的共享梯度中恢复训练图像.
    • 解决现有方法的局限性,使大批次大小的高分辨率图像重建能够实现,并且没有先前的数据知识.
    • 评估目前FL实践中与梯度共享相关的隐私风险.

    主要方法:

    • GIRG采用条件生成模型,直接从共享梯度中重建训练图像及其相关标签.
    • 该方法优化了生成模型的权重,而不是输入矢量,以产生准确的"虚拟"图像.
    • 吉尔格不需要任何关于客户的训练数据的预先信息来进行图像重建.

    主要成果:

    • 吉尔成功地从梯度中重建高分辨率图像,即使是大批量尺寸.
    • 该方法证明了能够从来自多个FL参与者的聚合梯度中恢复图像的能力.
    • 经验结果证实了GIRG在从共享梯度中恢复详细的图像信息方面的有效性.

    结论:

    • 拟议的GIRG方法对FL构成重大隐私风险,因为它可以从共享梯度中进行强大的图像重建.
    • 目前的FL实践仅依赖于梯度共享,容易受到复杂的反转攻击.
    • 迫切需要加强隐私保护机制,以减轻协作培训中的梯度逆转风险.