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相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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.
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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基于格鲁布斯测试在联合学习中的自适应模型过算法.

Wenbin Yao1,2, Bangli Pan1,2, Yingying Hou1,2

  • 1School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

联合学习 (FL) 易受毒害攻击的影响. 本研究介绍了FedGaf,一种自适应过算法,可以提高FL的稳定性和效率,特别是在非IID数据中.

关键词:
拜占庭-强壮-强大的联合学习的联合学习没有IID的非IID.毒药攻击 防御 防御

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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) 能够实现去中心化的模型培训,同时保持数据隐私.
  • FL系统容易受到中毒攻击,降低模型性能和可靠性.
  • 现有的防御系统难以平衡稳定性和效率,特别是与非独立和相同分布的 (非IID) 数据.

研究的目的:

  • 提出FedGaf,一个适应式模型过算法,用于联合学习.
  • 为了提高FL的强度和训练效率,防止中毒攻击.
  • 为了应对捍卫FL模型的权衡挑战,特别是在非IID数据集上.

主要方法:

  • 开发了一个基于Grubbs测试 (FedGaf) 的自适应模型过算法.
  • 设计了多个儿童自适应模型过算法,以优化稳定性和效率.
  • 实施了一个使用全球模型准确度的动态决策机制,以最大限度地降低计算开销.
  • 纳入全球模型加权聚合方法以加快模型的融合.

主要成果:

  • 与现有方法相比,FedGaf表现出优越的稳定性和效率.
  • 拟议的算法有效地防御各种中毒攻击策略.
  • 实验结果验证了FedGaf在IID和非IID数据集上的表现.
  • 在稳健性和效率之间,FedGaf比先前的防御实现了更好的权衡.

结论:

  • FedGaf提供了一种有效的解决方案,用于保护联合学习免受毒害攻击.
  • 该算法在稳定性和效率方面提供了显著的改进,特别是在非IID数据场景中.
  • 在确保联合学习系统方面,FedGaf是一个有前途的进步.