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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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...
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Distributed Loads01:19

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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.
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Maximum Size of Aggregate01:12

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Related Experiment Video

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

994

Decentralized Federated Learning With Distributed Aggregation Weight Optimization.

Zhiyuan Zhai, Xiaojun Yuan, Xin Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel distributed algorithm for decentralized federated learning (DFL) to optimize aggregation weights. This approach enables efficient, genuinely distributed model training on edge devices without central coordination.

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    994

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Decentralized federated learning (DFL) enables collaborative model training on edge devices via device-to-device (D2D) communication.
    • Aggregation weights are critical for DFL efficiency and accuracy but conventionally require central coordination.

    Purpose of the Study:

    • To develop a distributed algorithm for optimizing aggregation weights in DFL.
    • To align the weight optimization process with the decentralized nature of DFL.

    Main Methods:

    • A distributed aggregation weight optimization algorithm is developed for DFL.
    • Convergence is analyzed by quantifying the impact of weights on decentralized networks.
    • An eigenvalue optimization problem is formulated and solved using a subgradient-based algorithm.

    Main Results:

    • The proposed algorithm enables edge devices to obtain optimal aggregation weights using only local information.
    • Optimization, communication, and learning are all conducted in a distributed manner.
    • Numerical results show the algorithm's superiority in practical DFL deployments.

    Conclusions:

    • A genuinely distributed DFL system is achieved through a fully decentralized optimization approach.
    • The method enhances learning efficiency and accuracy in DFL settings.
    • The distributed algorithm effectively addresses the limitations of centralized weight optimization.