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Related Concept Videos

Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

552
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
552
Modeling and Similitude01:12

Modeling and Similitude

255
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

657
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
657
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

137
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
137
Horizontal Curve: Problem Solving01:03

Horizontal Curve: Problem Solving

49
A horizontal curve is characterized by its radius, intersection angle, and stationing of key points. In this case, the radius is 400 meters, and the angle of intersection is 30 degrees, with the station of the point of curvature (P.C.) at 0 + 150 meters. The goal is to determine the station values at the point of intersection (P.I.), point of tangency (P.T.), and midpoint of the curve, as well as the length of the long chord.The process begins with calculating the tangent distance (T) and the...
49
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
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.
In the absence...
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Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches.

Ismail M Elshair1, Tariq Jamil Saifullah Khanzada1,2, Muhammad Farrukh Shahid3

  • 1Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 29, 2024
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Summary

Federated learning (FL) enables privacy-preserving machine learning by training models locally. This study evaluates FL frameworks, finding trade-offs in performance and communication for combined horizontal and vertical approaches.

Keywords:
federated learningfederated learning simulatorsfederated learning topologyvertical and horizontal federated learning

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Area of Science:

  • Machine Learning
  • Decentralized Systems
  • Data Privacy

Background:

  • Federated learning (FL) trains models on local devices, avoiding centralized data collection for enhanced privacy and security.
  • FL relies on effective communication and collaboration between devices for scalability and robustness.
  • Existing FL frameworks require evaluation for diverse topologies and approaches.

Purpose of the Study:

  • To explore and evaluate various decentralized and centralized topologies in federated learning.
  • To compare the performance of four major end-to-end FL frameworks: FedML, Flower, Flute, and PySyft.
  • To investigate the combination of horizontal and vertical FL for common challenges like synchronization and communication overhead.

Main Methods:

  • Focused on horizontal and vertical federated learning systems.
  • Utilized a logistic regression model aggregated by the FedAvg algorithm.
  • Conducted experiments on MNIST and Fashion-MNIST image datasets to assess framework efficiency and performance.

Main Results:

  • Evaluated the efficiency and performance of FedML, Flower, Flute, and PySyft frameworks.
  • Identified trade-offs in performance across different federated learning simulation frameworks.
  • Provided initial findings on combining horizontal and vertical FL strategies.

Conclusions:

  • Federated learning offers a privacy-preserving alternative to centralized machine learning.
  • Framework selection involves performance and communication trade-offs, especially when combining FL approaches.
  • Further research can optimize FL strategies for practical, large-scale deployments.