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相关概念视频

Cognitive Learning01:21

Cognitive Learning

243
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
243
Introduction to Learning01:18

Introduction to Learning

408
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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
408
Observational Learning01:12

Observational Learning

179
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...
179
Machines: Problem Solving II01:30

Machines: Problem Solving II

310
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
310
Associative Learning01:27

Associative Learning

375
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...
375
Machines: Problem Solving I01:22

Machines: Problem Solving I

327
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
327

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相关实验视频

Updated: Jul 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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为资源有限的物联网提供个性化的公平分割学习.

Haitian Chen1,2,3, Xuebin Chen1,2,3, Lulu Peng1,2,3

  • 1College of Science, North China University of Science and Technology, Tangshan 063210, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

本研究介绍了针对资源有限的物联网 (IoT) 设备的个性化联合学习框架. 它提高了准确性,并确保在异质数据环境中公平分配利益.

关键词:
物联网的物联网,就是物联网.合作公平性 合作公平性数据异质性数据异质性联合学习的联合学习个性化模型个性化模型分拆学习是学习的分裂.

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相关实验视频

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 分布式系统 分布式系统

背景情况:

  • 联合学习 (FL) 对于物联网 (IoT) 中的隐私保护数据分析至关重要.
  • 由于有限的计算能力和存储,资源有限的物联网设备与传统的FL斗争.
  • 数据异质性和不平等的利益分配是物联网联合学习的重大挑战.

研究的目的:

  • 为资源受限的物联网客户提供个性化和公平的学习框架.
  • 为了实现高效的模型训练和边缘设备的个性化适应.
  • 确保在参与物联网设备之间公平地分配好处.

主要方法:

  • 采用U形模型结构,允许客户端将基础模型的部分卸载到中央服务器.
  • 客户在本地保留个性化的模型子集,以满足定制要求.
  • 优化模型聚合方法与基于贡献的权重用于公平的福利分配.

主要成果:

  • 与基线方法相比,拟议的框架在三个数据异质情景中实现了更高的准确性.
  • 该框架有效地解决了物联网环境中有限资源和数据异质性的挑战.
  • 成功实现了协作公平性,促进了设备之间的平衡合作.

结论:

  • 个性化和公平的分割学习框架为资源有限的物联网设备上的联合学习提供了可行的解决方案.
  • 这种方法提高了模型性能,并促进了分散的AI中公平的协作.
  • 它为物联网生态系统中更可持续,更有效的分布式学习铺平了道路.