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相关概念视频

Introduction to Learning01:18

Introduction to Learning

379
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...
379
Observational Learning01:12

Observational Learning

170
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...
170
Cognitive Learning01:21

Cognitive Learning

239
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...
239
Associative Learning01:27

Associative Learning

353
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...
353
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

645
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...
645
Multimachine Stability01:25

Multimachine Stability

151
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
151

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[Biocompatibility of silk fibroin nanofibers scaffold with olfactory ensheathing cells].

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[Association of tryptophan hydroxylase gene A218C and serotonin transporter gene polymorphism with essential hypertension in Chinese northern Han population].

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相关实验视频

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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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基于区块链的非IID数据的安全和分散的联合学习框架.

Feng Zhang1, Yongjing Zhang1, Shan Ji1

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.

Heliyon
|April 2, 2024
PubMed
概括

本研究引入了使用区块链的层次联合学习框架,以应对与非独立和相同分布 (非IID) 数据的挑战. 这种新的方法提高了模型的准确性,并确保了分散式机器学习中的隐私.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 区块链技术 区块链技术

背景情况:

  • 联合学习在没有数据共享的情况下促进了协作模式培训,但在与非独立和相同分布的 (非IID) 数据方面存在困难.
  • 跨组织的非IID数据分布显著挑战了传统的联合学习性能和模型准确性.

研究的目的:

  • 提出一个层次化的联合学习框架,利用区块链技术来增强非IID数据培训.
  • 为了改善数据隐私,安全性和分散环境中的整体联合学习性能.

主要方法:

  • 开发了一个区块链系统,以创建一个全球共享池,减少本地数据非IID程度,提高模型准确性.
  • 利用智能合约来实现分散的模型分布,收集和聚合在主区块链上.
  • 在MNIST,时尚-MNIST和CIFAR-10数据集上训练了多层感知器 (MLP) 和卷积神经网络 (CNN) 模型.

主要成果:

  • 拟议的框架显著提高了去中心化联合学习模型的准确性.
  • 在处理多个基准数据集的非IID数据方面表现出有效性.
  • 验证了基于区块链的分层联合学习方法的可行性和性能增强.

结论:

关键词:
区块链 区块链 区块链 区块链联合学习是联合学习.非IID数据的数据保护隐私 保护隐私 保护隐私智能合约是一种智能合约.

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  • 层次化的联合学习框架有效地解决了分散的环境中的非IID数据挑战.
  • 区块链集成增强了联邦学习中的隐私,安全和性能.
  • 拟议的方法为具有异质数据分布的协作机器学习提供了一个强大的解决方案.