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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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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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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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Systematic Sampling Method01:17

Systematic Sampling Method

11.1K
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.
Systematic sampling is one of the simplest methods...
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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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Maxpro Designs for Experiments with Multiple Types of Branching and Nested Factors.

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Updated: Sep 10, 2025

An Unbiased Approach of Sampling TEM Sections in Neuroscience
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An Unbiased Approach of Sampling TEM Sections in Neuroscience

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Aprendizaje federado semisupervisado con muestreo aleatorio uniforme y basado en redes de clientes

Mei Zhang1, Feng Yang2

  • 1School of Mathematics, Southwest Minzu University, Chengdu 610225, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje federado semisupervisado (Fed-SSL) se beneficia de estrategias de muestreo estructuradas. El muestreo basado en enrejado en FedAvg-SSL mejora la estabilidad y el rendimiento del entrenamiento en no i.i.d. datos comparados con los métodos aleatorios.

Palabras clave:
tasa de convergenciaAprendizaje federado y semisupervisadoAceleración linealparticipación parcial del clienteTécnicas casi Montecarlo

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Área de la Ciencia:

  • Inteligencia artificial
  • Aprendizaje automático
  • Sistemas distribuidos

Sus antecedentes:

  • El aprendizaje semi-supervisado federado (Fed-SSL) utiliza datos distribuidos etiquetados y sin etiqueta.
  • La participación parcial del cliente es común en Fed-SSL para reducir los gastos generales de comunicación.
  • No identificado Las distribuciones de datos plantean desafíos para las estrategias de muestreo de clientes en Fed-SSL.

Objetivo del estudio:

  • Proponer un nuevo algoritmo de aprendizaje semisupervisado de promedio federado, FedAvg-SSL.
  • Investigar el impacto de las diferentes estrategias de muestreo de clientes en el rendimiento de Fed-SSL.
  • Analizar las propiedades de convergencia del algoritmo propuesto.

Principales métodos:

  • Se introdujo FedAvg-SSL, que incorpora el muestreo aleatorio uniforme (Monte Carlo) y el muestreo basado en celosía (quasi-Monte Carlo).
  • Los clientes alternan entre la actualización del modelo global y el refinamiento de un modelo de pseudoetiqueta utilizando datos locales.
  • Proporcionó análisis teóricos de convergencia y realizó extensos experimentos.

Principales resultados:

  • FedAvg-SSL logra una tasa de convergencia sublineal con aceleración lineal.
  • El muestreo basado en la red demuestra ventajas sobre el muestreo aleatorio uniforme en el aprendizaje federado.
  • Los resultados experimentales validan los hallazgos teóricos y resaltan el impacto de las estrategias de muestreo.

Conclusiones:

  • FedAvg-SSL ofrece un enfoque eficaz para el aprendizaje federado semisupervisado.
  • El muestreo basado en enrejado mejora la estabilidad del entrenamiento y el rendimiento del modelo, especialmente bajo no-i.i.d. las condiciones.
  • El estudio proporciona información sobre la optimización de la participación y el muestreo de clientes para Fed-SSL.