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相关概念视频

Associative Learning01:27

Associative Learning

270
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.
Classical conditioning, also known...
270
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

66
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
66
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...
11.6K
Randomized Experiments01:13

Randomized Experiments

6.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.6K
Aggregates Classification01:29

Aggregates Classification

293
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...
293
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

617
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
617

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相关实验视频

Updated: May 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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基于量子化的链接,保护隐私的联合学习.

Ya Liu1,2, Shumin Wu3, Yibo Li3

  • 1The Department of Computer Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. liuya@usst.edu.cn.

Scientific reports
|May 16, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了Q-Chain FL,这是一个新的联合学习 (FL) 框架,可以提高隐私和效率. Q-Chain FL显著降低了分布式机器学习应用程序的通信开销和计算成本.

关键词:
联合学习 (FL)轻量化 轻量化 轻量化 轻量化 轻量化保护隐私 - 保护隐私量子化是指量化过程中的一个过程.

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科学领域:

  • 分布式机器学习 分布式机器学习
  • 数据隐私和安全数据隐私和安全

背景情况:

  • 联邦学习 (FL) 通过在当地培训模型来保护数据隐私.
  • 传统的FL面临的挑战是通信效率,计算成本和隐私保护,特别是在边缘计算中.
  • 高昂的开销阻碍了当前FL计划中的实时应用.

研究的目的:

  • 提出一个创新的联合学习框架,Q-Chain FL.
  • 解决传统FL的通信和计算开销挑战.
  • 在分布式学习中增强隐私保护和模型融合速度.

主要方法:

  • 将量子化压缩技术集成到链式FL架构 (Q-Chain FL) 中.
  • 在用户节点有效压缩和传输模型参数差异.
  • 在服务器节点上的参数的无解压和聚合.

主要成果:

  • 在Q-Chain FL中,通信和计算开销较低.
  • 该框架实现了跨多个数据集 (MNIST,CIFAR-10,CelebA) 的快速融合速度和高安全性.
  • 通信开销减少了约62.5% (相对于FedAvg) 和44.7% (相对于链式PPFL).

结论:

  • Q-Chain FL为联合学习提供了一个强大的,可适应的解决方案.
  • 拟议的框架有效地减轻了隐私风险,同时提高了效率.
  • 结果突出了Q-Chain FL在现实世界的分布式学习场景中的潜力.