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相关概念视频

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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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 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
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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基于区块链的安全联合学习与本地差异性隐私和激励.

Saptarshi DE Chaudhury1, Likhith Reddy Morreddigari1, Matta Varun1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.

IEEE transactions on privacy
|January 13, 2025
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概括
此摘要是机器生成的。

本研究介绍了使用区块链和局部差异隐私 (LDP) 的联合学习 (FL) 的新方法. 它鼓励数据共享,确保只有贡献节点才能访问受过训练的模型,增强安全性和参与性.

关键词:
加密的模型参数.超级账本 (HyperLedger) 的织物是如此.联合学习的联合学习当地差异性隐私 地方差异性隐私一个会话密钥,会话密钥.

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科学领域:

  • 区块链技术 区块链技术
  • 机器学习 机器学习
  • 网络安全 网络安全

背景情况:

  • 联邦学习 (FL) 的采用正在增长,但参与者对受训模型的安全访问仍然是一个重大挑战.
  • 现有的FL系统难以将模型访问限制在仅限于积极贡献者的范围内,这带来了安全性和公平性的问题.
  • 当地差异隐私 (LDP) 提供数据模糊,但需要与激励机制集成,以实现有效的FL.

研究的目的:

  • 根据地方差异隐私 (LDP) 在联邦学习 (FL) 中激励模型参数共享的新方法.
  • 确保只有积极参与的节点才能访问更新的全球模型,解决当前FL系统中的一个关键挑战.
  • 利用区块链技术来安全,分散地管理FL流程和模型访问.

主要方法:

  • 开发了一个基于代币的激励机制,在LDP下共享较少模糊数据的节点可以获得更多的代币.
  • 利用HyperLedger Fabric (HLF),一个许可区块链,用于本地参数共享和全球参数更新.
  • 在HLF内实施了链代码 (智能合约) 来管理代币分配和模型访问控制.

主要成果:

  • 在LDP下共享较少扰乱数据的节点得到了代币的奖励,从而可以访问加密的模型参数.
  • 贡献较少或共享高度扰乱数据的节点取较少的代币,可能限制它们访问更新的全球模型.
  • 实验结果证明了拟议的基于区块链的FL方法的可行性和有效性.

结论:

  • 拟议的方法成功地激励了使用区块链在LDP启用FL中的模型参数共享.
  • 对受训模型的访问控制得到有效管理,确保只有贡献节点才能检索更新的参数.
  • 基于区块链的方法提高了安全性,减轻了单点故障,并验证了系统的可行性.