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相关概念视频

F Distribution01:19

F Distribution

3.7K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
3.7K
Ratio Level of Measurement00:54

Ratio Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.5K
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

88
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
88
Measurement: Derived Units03:02

Measurement: Derived Units

42.9K
The International System of Units or SI system, by international agreement, has fixed measurement units for seven fundamental properties: length, mass, time, temperature, electric current, amount of substance, and luminosity. These are called the SI base units.
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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相关实验视频

Updated: Jun 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

533

对于联合学习的公平贡献测量方法

Peng Guo1, Yanqing Yang1, Wei Guo2

  • 1School of Computer Science and Technology (School of Cyberspace Security), Xinjiang University, Urumqi 830046, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个公平的联合学习贡献测量方案,以促进客户参与. 它使用一种新的聚合权重来准确评估参与者的贡献,即使使用非IID数据,也可以提高模型的准确性,而不会增加计算时间.

关键词:
没有IID的非IID.沙普利的价值是什么意思贡献衡量的贡献测量.联合学习的联合学习

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

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相关实验视频

Last Updated: Jun 17, 2025

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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科学领域:

  • 机器学习 机器学习
  • 数据 隐私 数据 隐私 数据
  • 合作游戏理论 合作游戏理论

背景情况:

  • 联合学习 (FL) 增强了数据隐私和安全性,但在积极的客户参与方面存在困难.
  • 现有的Shapley基于价值的贡献评估方法在FL是计算昂贵和不切实际的,因为额外的模型培训.
  • 在FL中的非独立和相同分布 (非IID) 数据会对全球模型准确性和参与者贡献测量产生负面影响.

研究的目的:

  • 开发一个公平的联合学习贡献测量方案,避免额外的模型计算.
  • 准确测量参与者在联合学习中的贡献,特别是解决非IID数据带来的挑战.
  • 提高联合学习模型的整体准确性和效率.

主要方法:

  • 引入了一种新的聚合权重,以提高联合学习中贡献测量的准确性.
  • 开发了一个公平的联合学习贡献测量方案,绕过了额外模型计算的需要.
  • 在MNIST和时尚MNIST数据集上评估了该计划的性能.

主要成果:

  • 拟议的方法准确计算了参与者在联合学习场景中的贡献.
  • 实验结果显示,与现有的基线算法相比,模型准确度显著提高.
  • 新方案实现了与现有方法相比的时间成本,同时提高了准确性.

结论:

  • 开发的公平联合学习贡献测量方案有效地解决了现有方法的局限性.
  • 新的聚合权重成功地提高了贡献测量的准确性,即使使用异质 (非IID) 数据.
  • 这种方法提供了一种实用和有效的解决方案,以鼓励客户参与并提高联合学习绩效.