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相关概念视频

Concepts and Prototypes01:24

Concepts and Prototypes

95
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
37.5K
Observational Learning01:12

Observational Learning

131
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

Updated: Jun 4, 2025

The Modified Temptation Resistance Task: A Paradigm to Elicit Children's Strategic Lie-telling
06:51

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Published on: April 6, 2018

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联邦警察局:保护联合原型学习免受后门攻击.

Zhou Tan1, Jianping Cai1, De Li2

  • 1College of Computer Science and Big Data, Fuzhou University, Fuzhou, 350000, China.

Neural networks : the official journal of the International Neural Network Society
|December 21, 2024
PubMed
概括
此摘要是机器生成的。

使用新的FedPD框架,可以保护联邦学习 (FL) 免受后门攻击. 这种方法交换原型而不是模型参数,增强安全性并减少分布式机器学习的通信开销.

关键词:
后门攻击是通过后门进行的.联合学习是联合学习.非IID数据的数据原型网络的原型网络

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相关实验视频

Last Updated: Jun 4, 2025

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 网络安全 网络安全

背景情况:

  • 联合学习 (FL) 能够在保护数据隐私的同时实现协作模式培训.
  • 后门攻击通过操纵模型预测,在FL构成重大威胁.
  • 现有的防范后门攻击在FL造成高的计算和通信成本,特别是在非IID设置.

研究的目的:

  • 提出一个新的防御框架,FedPD,针对联邦学习的后门攻击.
  • 为了减少与在FL中防御恶意客户相关的开销.
  • 为了保持主要任务的高精度,同时减轻后门威胁.

主要方法:

  • 该 FedPD 框架促进了服务器和客户端之间的原型交换,而不是模型参数.
  • 这种基于原型的通信本质上阻止恶意参与者插入后门通道.
  • 原型还可以作为全球知识的来源来完善当地客户的培训.

主要成果:

  • 与现有的方法相比,FedPD表现出优越和一致的防御性能,可以抵御后门攻击.
  • 该框架通过交换原型,显著降低了通信开销.
  • 与未受保护的FedAvg相比,FedPD实现了90.73%的攻击成功率降低,同时保持了90%以上的主要任务准确性.

结论:

  • 通过防止后门通道植入,FedPD有效地消除了后门攻击的来源.
  • 拟议的方法适用于资源有限的环境和非IID数据分布.
  • 联邦学习系统 (FedPD) 提供了一种强大而高效的解决方案,用于保护联邦学习系统免受敌对威胁.