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相关概念视频

Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Distributed Loads01:19

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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基于ECC的轻量级自愈联合学习框架,用于安全的IIoT网络

Mikail Mohammed Salim1, Farheen Naaz1, Kwonhue Choi2

  • 1School of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

Leash-FL增强了工业物联网 (IIoT) 的联合学习,使用轻量级加密和区块链来提高弹性. 这种框架可以实现对恶意客户端的高精度,并使得快速,安全的恢复,提高IIoT安全性.

关键词:
物联网安全物联网安全物联网安全区块链安全区块链安全区块链安全圆曲线密码学 圆曲线密码学联合学习的联合学习网络安全 网络安全保护隐私的身份验证自愈系统自愈系统

相关实验视频

Last Updated: May 2, 2026

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科学领域:

  • 网络安全 网络安全
  • 分布式系统 分布式系统
  • 机器学习 机器学习

背景情况:

  • 工业物联网 (IIoT) 中的联合学习使协作智能成为可能,但引入了身份伪造和模型中毒等漏洞.
  • 资源有限的IIoT环境需要轻量级但强大的安全解决方案来减轻这些风险.

研究的目的:

  • 介绍Leash-FL,一个新的自我修复框架,旨在提高IIoT网络中联合学习的弹性.
  • 解决安全挑战,包括身份验证,数据完整性和攻击后快速恢复.

主要方法:

  • 整合无证书的圆曲线加密 (CECC) 以实现高效,无法链接的身份验证,并使用假名旋转.
  • 实施类似性控制的选机制来过恶意更新.
  • 利用区块链用于可审计性和检查点回滚恢复.
  • 管理会员变化,并提供前后保密保证.

主要成果:

  • 在50%的恶意客户中,Leash-FL保持了超过85%的准确性,并将后门成功率降低到5%以下.
  • 恢复速度高达基线方法的三倍,会员资格的变化在60毫秒内得到管理.
  • 区块链层展示了低延迟,高吞吐量和高效的分类账管理.

结论:

  • Leash-FL通过结合轻量级身份验证,区块链可审计性和自我修复恢复,为IIoT提供安全,弹性和可扩展的联合学习解决方案.
  • 该框架有效地减轻了在资源有限的环境中常见的联合学习威胁.