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Updated: Nov 3, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Communication-Censored Distributed Stochastic Gradient Descent.

Weiyu Li, Zhaoxian Wu, Tianyi Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |June 4, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a communication-censored distributed stochastic gradient descent (CSGD) algorithm. CSGD significantly reduces data transmission in distributed networks by only sending informative gradients, saving communication costs.

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    Last Updated: Nov 3, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
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    Area of Science:

    • Distributed Systems
    • Optimization Algorithms
    • Machine Learning

    Background:

    • Distributed optimization is crucial for large-scale applications like machine learning.
    • Existing methods for reducing communication in distributed settings include quantization and sparsification.
    • These methods can introduce noise or complexity in the optimization process.

    Purpose of the Study:

    • To develop a communication-efficient algorithm for stochastic optimization in distributed networks.
    • To reduce the communication overhead in distributed machine learning applications.
    • To introduce a novel communication-censoring technique for distributed gradient descent.

    Main Methods:

    • Developed a communication-censored distributed stochastic gradient descent (CSGD) algorithm.
    • Implemented a strategy where gradients are transmitted only if sufficiently informative.
    • Employed an increasing batch size to mitigate the impact of gradient noise.
    • Utilized stale gradients at the server when new ones are not available.

    Main Results:

    • CSGD achieves the same order of convergence rate as standard stochastic gradient descent (SGD).
    • The algorithm effectively reduces communication transmissions between workers and the server.
    • Numerical experiments confirm substantial communication savings.

    Conclusions:

    • CSGD offers a communication-efficient alternative for distributed stochastic optimization.
    • The communication-censoring technique is effective in reducing network burden.
    • This approach is promising for large-scale distributed machine learning.