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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

781
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...
781
Associative Learning01:27

Associative Learning

784
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...
784
Observational Learning01:12

Observational Learning

528
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...
528
Introduction to Learning01:18

Introduction to Learning

665
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...
665
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

863
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...
863
Distributed Loads01:19

Distributed Loads

738
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
738

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Related Experiment Videos

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing.

Yinghui Liu1, Youyang Qu2, Chenhao Xu2

  • 1School of Information Technology, Deakin University, Burwood, VIC 3125, Australia.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Federated learning with asynchronous convergence (FedAC) enhances machine learning efficiency and security by using blockchain for model aggregation. This approach addresses privacy concerns and improves performance on edge devices.

Keywords:
asynchronous convergenceblockchainedge computingfederated learning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Distributed Systems
  • Cybersecurity

Background:

  • The proliferation of edge computing generates vast data, driving machine learning (ML) development.
  • Privacy concerns in data collection for ML are significant.
  • Synchronous federated learning (FL) offers a solution but suffers from inefficiency due to diverse device capabilities and vulnerability to single-point failures and attacks.

Purpose of the Study:

  • To propose an innovative federated learning method, federated learning with asynchronous convergence (FedAC), that enhances efficiency and security.
  • To address the limitations of synchronous FL, including training time discrepancies and centralized vulnerabilities.

Main Methods:

  • Developed FedAC, incorporating a staleness coefficient for asynchronous convergence.
  • Utilized a blockchain network for decentralized global model aggregation, replacing traditional central servers.
  • Implemented and evaluated FedAC on the MNIST dataset for both horizontal and vertical FL.

Main Results:

  • Achieved high accuracy rates of 98.96% in horizontal FL and 95.84% in vertical FL on the MNIST dataset.
  • Demonstrated FedAC's superiority over baseline models in extensive evaluations.
  • Showcased resilience against issues like abnormal local device failures and dedicated attacks.

Conclusions:

  • FedAC offers a robust and efficient solution for privacy-preserving machine learning on edge devices.
  • The integration of blockchain technology enhances the security and reliability of federated learning.
  • FedAC represents a significant advancement in overcoming the challenges of current federated learning frameworks.