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Related Concept Videos

Associative Learning01:27

Associative Learning

303
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...
303
Observational Learning01:12

Observational Learning

145
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...
145
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

458
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
458
Metacognition01:26

Metacognition

141
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
141
Cognitive Learning01:21

Cognitive Learning

223
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...
223
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

83
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Related Experiment Video

Updated: Jun 10, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
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Federated learning: Overview, strategies, applications, tools and future directions.

Betul Yurdem1, Murat Kuzlu2, Mehmet Kemal Gullu1

  • 1Department of Electrical and Electronics Engineering, Izmir Bakircay University, Izmir, Turkey.

Heliyon
|October 11, 2024
PubMed
Summary
This summary is machine-generated.

Federated learning (FL) enables collaborative model training without sharing raw data, enhancing privacy and security. This approach offers scalable solutions for various applications, addressing key confidentiality concerns.

Keywords:
Data privacyDistributed machine learningFederated learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Distributed Systems

Background:

  • Federated learning (FL) is a distributed machine learning paradigm.
  • It enables collaborative training of a shared model across multiple decentralized nodes.
  • FL ensures data privacy by keeping raw data localized and only sharing model updates.

Purpose of the Study:

  • To provide a comprehensive review of federated learning.
  • To cover its principles, strategies, applications, and tools.
  • To identify opportunities, challenges, and future research directions in FL.

Main Methods:

  • Literature review of federated learning principles and strategies.
  • Analysis of existing federated learning applications and tools.
  • Discussion of challenges and future research avenues.

Main Results:

  • Federated learning significantly enhances data privacy and security.
  • Key advantages include scalability and efficiency in distributed environments.
  • FL strategies are particularly beneficial for high-risk applications requiring confidentiality.

Conclusions:

  • Federated learning is a powerful approach for privacy-preserving machine learning.
  • It addresses critical data confidentiality concerns in sensitive domains.
  • Further research is needed to explore its full potential and overcome existing challenges.