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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
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FedADT:一种基于衍生术语的自适应方法,用于联合学习.

Huimin Gao1, Qingtao Wu1,2, Xuhui Zhao1

  • 1School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

基于衍生术语 (FedADT) 的联邦自适应学习通过使用自适应步骤和梯度差异来改进联合学习. 这种新的方法增强了模型的融合,并减少了分布式培训中的噪音敏感性.

关键词:
一个衍生品的衍生品.分布式培训是指分布式培训.联合学习的联合学习随机梯度的渐变 随机梯度的渐变

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科学领域:

  • 机器学习 机器学习
  • 分布式系统 分布式系统
  • 计算机视觉 计算机视觉

背景情况:

  • 联合学习可以在本地数据上进行协作模型培训.
  • 挑战包括标准联合学习中的缓慢融合,缺乏适应性和噪音敏感性.

研究的目的:

  • 引入基于衍生术语 (FedADT) 的联合适应性学习,以解决联合学习的局限性.
  • 在分布式培训中增强融合速度和对噪声的稳定性.

主要方法:

  • 在本地模型更新中,FedADT集成了适应性步骤大小和梯度差异.
  • 移动平均线的衰减应用于衍生项,以减轻噪音.
  • 对非凸的客观函数进行了收分析.

主要成果:

  • 通过适当的超参数调整,FedADT可以达到1/nT的合率.
  • 关于图像分类 (MNIST,时尚MNIST) 的实验证明了FedADT的有效性.
  • 接收器运行特征曲线验证了服装类别预测中的性能.

结论:

  • FedADT提供了一种有效的解决方案,用于提高联合学习绩效.
  • 拟议的方法提高了分布式培训环境中的融合和噪声稳定性.