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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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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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Distributed Loads: Problem Solving01:21

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Related Experiment Video

Updated: Jan 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

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Federated Learning over MU-MIMO Vehicular Networks.

Maria Raftopoulou1,2, José Mairton B da Silva3, Remco Litjens1,2

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

Federated learning in vehicular networks can be optimized by selecting vehicles based on their importance and resource usage. Multi-user MIMO enhances model convergence and faster accuracy in traffic sign classification.

Keywords:
MU-MIMOfederated learningresource allocationvehicle selectionvehicular networkswireless networks

Related Experiment Videos

Last Updated: Jan 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.5K

Area of Science:

  • Vehicular networks
  • Machine learning
  • Wireless communication

Background:

  • Federated learning (FL) offers improved accuracy for vehicular applications by leveraging data from multiple vehicles.
  • Challenges in vehicular FL include limited bandwidth, variable channel quality, and latency, impacting vehicle selection and resource allocation.
  • Optimizing FL requires characterizing vehicles by learning importance and wireless resource utilization.

Purpose of the Study:

  • To address the joint vehicle selection and resource allocation problem in multi-cell vehicular networks.
  • To develop an efficient algorithm for optimizing federated learning in vehicular environments.
  • To evaluate the impact of multi-user MIMO on FL performance for traffic sign classification.

Main Methods:

  • Characterized participating vehicles based on learning importance and wireless resource usage.
  • Formulated a joint vehicle selection and resource allocation optimization problem for multi-cell MU-MIMO networks.
  • Proposed a "vehicle-beam-iterative" algorithm to approximate the optimization solution.
  • Conducted extensive simulations using realistic road and mobility models for traffic sign object classification.

Main Results:

  • Multi-user MIMO (MU-MIMO) was shown to significantly improve the convergence time of the global federated learning model.
  • Application-specific accuracy targets were achieved faster in scenarios with uniform training data set sizes across vehicles compared to varied sizes.
  • The proposed "vehicle-beam-iterative" algorithm effectively approximated the solution to the complex optimization problem.

Conclusions:

  • The study demonstrates the effectiveness of joint vehicle selection and resource allocation in enhancing federated learning for vehicular applications.
  • MU-MIMO technology is crucial for improving the efficiency and performance of federated learning in vehicular networks.
  • Future research can explore adaptive strategies for varying data set sizes to further optimize federated learning performance in dynamic vehicular environments.