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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.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Observational Learning01:12

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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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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Associative Learning01:27

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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.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Federated Learning in Edge Computing: A Systematic Survey.

Haftay Gebreslasie Abreha1, Mohammad Hayajneh1, Mohamed Adel Serhani1

  • 1Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

Federated Learning (FL) addresses Edge Computing (EC) challenges like privacy and bandwidth by enabling collaborative model training on local data. This survey systematically reviews FL implementations and challenges in EC environments.

Keywords:
data privacydata securityedge AIedge computingfederated learningintelligent edge

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge Computing (EC) extends Cloud Computing (CC) closer to data sources, enabling Deep Learning (DL) applications.
  • Conventional DL architectures in EC face bandwidth, privacy, and legalization issues due to frequent data sharing.
  • Federated Learning (FL) offers a solution by enabling collaborative model training across distributed clients while preserving data localization.

Purpose of the Study:

  • To systematically survey the literature on Federated Learning (FL) implementation within Edge Computing (EC) environments.
  • To provide a taxonomy of advanced solutions and identify open problems in FL for EC.
  • To offer a comprehensive understanding of FL and EC integration for researchers.

Main Methods:

  • Review of fundamental concepts of Edge Computing (EC) and Federated Learning (FL).
  • Systematic review of existing research on FL in EC environments.
  • Analysis of protocols, architecture, frameworks, and hardware requirements for FL in EC.
  • Discussion of applications, challenges, and existing solutions for edge FL.
  • Inclusion of two case studies on FL application in EC.

Main Results:

  • Identification of key protocols, architectures, and hardware for FL in EC.
  • Discussion of prevalent applications and challenges in edge FL.
  • Presentation of existing solutions and future research directions.
  • Highlighting the benefits of FL in overcoming EC limitations for DL.

Conclusions:

  • FL is a crucial enabler for privacy-preserving and efficient collaborative learning in Edge Computing (EC).
  • This survey provides a structured overview of FL in EC, identifying gaps and future research avenues.
  • Understanding FL implementation and challenges is vital for advancing EC-enabled AI applications.