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Related Concept Videos

Associative Learning01:27

Associative Learning

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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...
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Observational Learning01:12

Observational Learning

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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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Cognitive Learning01:21

Cognitive Learning

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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...
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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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Related Experiment Videos

Federated Learning with Assured Privacy and Reputation-Driven Incentives for Internet of Vehicles.

Jiayong Chai1, Mo Chen2, Wei Zhang3

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

The FLARE architecture enhances cross-domain data collaboration for intelligent systems by integrating privacy-preserving federated learning (FL) and blockchain incentives. It ensures data security and boosts participation quality in vehicle data applications.

Keywords:
Internet of Thingsblockchainfederated learninggradient privacyincentive mechanismzero-knowledge proof

Related Experiment Videos

Area of Science:

  • * Intelligent Transportation Systems
  • * Data Security and Privacy
  • * Machine Learning

Background:

  • * Cross-domain data collaboration is crucial for intelligent systems like the Internet of Vehicles, but data silos and privacy concerns hinder progress.
  • * Existing Federated Learning (FL) and Blockchain solutions offer traceability but suffer from privacy leakage risks and ineffective incentives.
  • * Lack of trust and delayed rewards reduce participation quality in collaborative data initiatives.

Purpose of the Study:

  • * To propose the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture.
  • * To address privacy leakage and incentive mechanism limitations in current cross-domain data collaboration schemes.
  • * To enhance trust, security, and participation quality in data-sharing environments.

Main Methods:

  • * Development of the FLARE architecture integrating cryptographic security and mechanism design.
  • * Implementation of the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol using partial homomorphic encryption and zero-knowledge proofs.
  • * Design of the Economy-on-Chain incentive (EconChain) mechanism with instant micro-rewards and a dynamic reputation model.

Main Results:

  • * FLARE provides verifiable privacy guarantees for gradient contributions, defending against inversion attacks.
  • * The EconChain mechanism ensures precise measurement and sustainable incentivization of training contributions.
  • * Experiments demonstrate FLARE enhances node participation and contribution quality without sacrificing model accuracy.

Conclusions:

  • * FLARE offers a novel paradigm for trusted and efficient cross-domain data circulation.
  • * The architecture effectively balances strong security with high vitality in collaborative learning.
  • * FLARE improves participant engagement and data quality in sensitive applications like intelligent transportation.