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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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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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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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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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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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Related Experiment Video

Updated: May 29, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Federated learning with bilateral defense via blockchain.

Jue Xiao1, Hewang Nie1, Zepu Yi1

  • 1School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 1, 2025
PubMed
Summary

This study introduces FedBASS, a novel Federated Learning scheme using blockchain and dual servers to defend against privacy breaches and poisoning attacks. FedBASS enhances model security and privacy, even with non-IID data and untrusted participants.

Keywords:
BlockchainDifferential privacyFederated learningHomomorphic encryptionRobustness

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Area of Science:

  • Artificial Intelligence
  • Cybersecurity
  • Distributed Systems

Background:

  • Federated Learning (FL) enhances data privacy but faces security threats like data breaches and poisoning attacks.
  • Existing FL solutions often address only one security issue, leaving a gap in comprehensive defense.
  • A non-fully trusted model with malicious clients and honest-but-curious servers presents a complex security challenge.

Purpose of the Study:

  • To propose a robust Federated Learning scheme, FedBASS, that addresses both privacy breaches and model poisoning attacks.
  • To develop a system resilient to non-IID data and secure against both malicious clients and servers.
  • To ensure transparency, robustness, and practicality in Federated Learning environments.

Main Methods:

  • Implemented a dual-server architecture (Analyzer and Verifier) with CKKS encryption for gradient security.
  • Utilized cosine similarity for detecting malicious clients and a gradient compensation strategy for non-IID data.
  • Integrated a weakened differential privacy scheme with shuffling for privacy during clustering and employed blockchain for communication integrity.

Main Results:

  • FedBASS effectively mitigates privacy breaches and poisoning attacks in a challenging threat model.
  • The scheme demonstrates robustness against non-IID data through dynamic clustering and gradient compensation.
  • Blockchain integration ensures transparency and prevents selfish behaviors, enhancing overall system integrity.

Conclusions:

  • FedBASS successfully balances model fidelity, robustness, efficiency, and privacy in Federated Learning.
  • The proposed scheme offers a practical and secure solution for complex, untrusted environments.
  • This work advances Federated Learning security by addressing multiple threats simultaneously.