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相关概念视频

Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

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基于两阶段梯度修剪和差异化差异化隐私的高效通信,保护隐私的联合学习算法.

Yong Li1,2,3, Wei Du1, Liquan Han1

  • 1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括

本研究介绍了IsmDP-FL,这是一种新的联合学习 (FL) 算法,可以提高通信效率并保持模型隐私. 它使用双阶段梯度修剪和差异化的差异隐私来降低成本和保护敏感数据.

关键词:
差异化的差异化隐私 差异化的隐私联合学习的联合学习渐变修剪 渐变修剪 渐变修剪维护隐私 维护隐私 维护隐私

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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) 面临着安全和高通讯成本的挑战.
  • 不同隐私 (DP) 保护数据,但可以降低模型的准确性.
  • 现有的模型修剪方法旨在减少通讯开销.

研究的目的:

  • 开发一种高效的通信和保护隐私的FL算法.
  • 为了解决隐私保护和模型准确性之间的权衡问题,FL.
  • 为了减少在训练大规模联合模型的通信负担.

主要方法:

  • 介绍了IsmDP-FL,这是一个两阶段的联合学习算法.
  • 采用了梯度修剪和差异化的差异隐私.
  • 应用DP在修剪后的重要参数和网络层中的剩余参数.

主要成果:

  • 在广泛的实验中证明了高的通信效率.
  • 在整个联合学习过程中成功维护模型隐私.
  • 减少了隐私预算的不必要消耗.

结论:

  • 在联合学习中,IsmDP-FL有效地平衡了隐私和沟通效率.
  • 拟议的方法为安全和高效的大规模模型培训提供了一个实际的解决方案.
  • 这种方法最大限度地减少了隐私预算的使用,而不会影响模型性能.