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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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...
Aggregates Classification01:29

Aggregates Classification

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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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相关实验视频

Updated: Jun 9, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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适应性数据集管理方案用于移动边缘计算中的轻量级联合学习.

Jingyeom Kim1, Juneseok Bang1, Joohyung Lee1

  • 1School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

由于计算能力有限,移动设备上的联合学习 (FL) 面临着挑战. 本研究引入了一个自适应数据集管理 (ADM) 方案,以减少当地培训负担,改善对物联网的参与.

关键词:
数据集管理数据集管理联合学习的联合学习移动边缘计算移动边缘计算

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科学领域:

  • 机器学习 机器学习
  • 移动边缘计算 移动边缘计算
  • 物联网的物联网,就是物联网.

背景情况:

  • 联合学习 (FL) 允许跨移动设备 (MD) 的协作模式培训,而无需数据暴露.
  • FL减轻了中央服务器的负担,但对能力有限的MDs强加了大量的本地培训计算.

研究的目的:

  • 提出一个适应性数据集管理 (ADM) 计划,以减少FLMD的当地培训负担.
  • 应对MDs有限的计算能力的挑战,阻碍其对FL的贡献.

主要方法:

  • 实证研究数据集大小对沟通轮的准确性收益的影响.
  • 引入一个折扣因子,表示数据集大小对准确性的减少影响.
  • 为ADM问题制定理论框架,考虑折扣因子和Kullback-Leibler分歧 (KLD).
  • 基于贪的启发式算法的建议,以解决非凸的ADM优化问题.

主要成果:

  • 证实数据集大小对FL的准确性收益的影响正在减少.
  • 拟议的ADM计划有效地减少了MD的培训负担.
  • 启发式算法提供了一个低复杂度的亚最佳解决方案.

结论:

  • 该ADM计划成功地减轻了佛罗里达州MD的当地培训负担.
  • 保持可接受的训练准确度,同时降低移动设备的计算需求.
  • 该方法提高了在资源有限的物联网环境中FL的可行性.