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相关概念视频

Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
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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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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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相关实验视频

Updated: Sep 9, 2025

Sampling Soils in a Heterogeneous Research Plot
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在联合学习中实现真实分布模拟的强大采样技术

Robin Hoepp1,2, Leonhard Rist3,4, Alexander Katzmann3

  • 1Computed Tomography, Siemens Healthineers, Forchheim, Germany. robin.hoepp@fau.de.

International journal of computer assisted radiology and surgery
|September 2, 2025
PubMed
概括

联合学习 (FL) 培训可能会受到非IID数据的损害. 一个新的采样算法模拟了现实的标签分布,以便在部署之前分析FL性能恶化.

关键词:
分配转移联合学习采样情况

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科学领域:

  • 机器学习
  • 人工智能
  • 医疗信息学

背景情况:

  • 联邦学习 (FL) 能够在分散的数据上培训深度学习模型,这对于敏感的临床环境至关重要.
  • 由于客户之间的人口差异,非独立且相同分布的 (非IID) 数据可能会显著降低FL模型的性能.
  • 在医疗保健中实施大规模FL之前,评估非IID数据分布的影响至关重要.

研究的目的:

  • 开发和评估一种新的抽样算法,以创建现实的,以客户为导向的标签分配.
  • 在模拟的非IID数据场景下调查FL模型的性能下降.
  • 提供一种有效的方法来分析FL数据异质性的影响.

主要方法:

  • 一个采样算法被开发出来,用于从全球分布中生成特定的平均值和标准偏差的数据子集.
  • 用奇平方和吉尼杂质量为多个组的标签分布的数值优化.
  • 该算法应用于现实世界的临床数据集,用于基于3D摄像头的体重和身高估计.

主要成果:

  • 采用采样非IID数据的联邦平均值 (FedAvg) 训练导致绩效下降.
  • 在全球模型中,体重估计下降了25.3%,身高估计下降了28.7%.
  • 与硬数据分割基线相比,拟议的采样技术显示出显著的负面影响.

结论:

  • 在FL环境中以客户为导向的标签分发可能会严重损害模型的训练和性能.
  • 开发的采样算法为非IID数据效应的部署前分析提供了有效的方法.
  • 这种技术具有多样性,适用于各种网络架构,临床场景和非IID亚群.