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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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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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HADA: A Hybrid Authentication and Dynamic Attribute Access Control Mechanism for the Internet of Things Using Hyperledger Fabric Blockchain.

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相关实验视频

联合学习中的物联网身份验证:方法,挑战和未来方向

Arwa Badhib1, Suhair Alshehri1, Asma Cherif1,2

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
概括

联合学习 (FL) 能够在相互连接的设备上进行安全的数据分析. 本研究调查了FL的身份验证方法,解决了诸如模型中毒等安全威胁,并加强了对人工智能系统的信任.

关键词:
这就是为什么物联网是物联网物联网.认证的真实性 认证的真实性行为行为行为行为行为.生物识别系统是生物识别系统.区块链区块链区块链区块链区块链密码学 密码学 密码学联合学习的联合学习.

相关实验视频

科学领域:

  • 物联网 (IoT) 安全问题
  • 机器学习隐私 机器学习隐私
  • 分布式人工智能 分布式人工智能

背景情况:

  • 物联网产生了大量数据,通过机器学习进行分析,但集中式培训引发了隐私问题.
  • 联邦学习 (FL) 在本地培训模型,只分享更新,减轻隐私问题.
  • FL系统面临着模型中毒和拜占庭式攻击等安全威胁,需要强大的身份验证.

研究的目的:

  • 为联邦学习 (FL) 中的认证机制提供全面的调查.
  • 检查FL认证流程,挑战和架构方面的考虑.
  • 根据技术和系统背景对现有的FL认证方案进行分类.

主要方法:

  • 审查和评估现有的FL认证方案.
  • 基于启用技术 (区块链,密码学,人工智能) 和系统背景的方案的分类.
  • 分析数据集,实验环境,并识别研究缺口.

主要成果:

  • 现有的认证方案在有效性,局限性和实用性方面各不相同.
  • 分类提供了当前方法的结构化概述.
  • 确定了开放研究的挑战和未来的方向,以确保安全的FL.

结论:

  • 强大的身份验证对于安全可信的联合学习至关重要.
  • 这项调查为推进FL安全提供了基础参考.
  • 需要进一步的研究来应对已识别的挑战,并提高FL的稳定性.