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Updated: Oct 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Model Compression for Communication Efficient Federated Learning.

Suhail Mohmad Shah, Vincent K N Lau

    IEEE Transactions on Neural Networks and Learning Systems
    |December 22, 2021
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    Summary
    This summary is machine-generated.

    This study introduces compression techniques to create sparse deep neural network models for federated learning, reducing data transmission and computational needs. The proposed methods enhance model efficiency without sacrificing accuracy.

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    Area of Science:

    • Machine Learning
    • Deep Learning
    • Federated Learning
    • Model Compression

    Background:

    • Deep neural networks (DNNs) offer advantages over shallow networks but face challenges in federated learning (FL) due to large model sizes and high computational demands.
    • Large DNNs in FL settings can lead to infeasible data transmission volumes between servers and clients, hindering training efficiency and scalability.
    • Existing FL approaches struggle to balance model complexity with communication constraints.

    Purpose of the Study:

    • To investigate traditional and novel compression techniques for constructing sparse deep neural network models suitable for federated learning.
    • To address the limitations of large storage and computational requirements in federated deep learning.
    • To reduce data transmission volume for both server-to-client (downstream) and client-to-server (upstream) communication.

    Main Methods:

    • Exploration of traditional and novel compression techniques to create sparse models from dense deep neural networks.
    • Separate consideration of compression strategies for server-side (downstream) and client-side (upstream) models.
    • Development of methods to establish and maintain model sparsity throughout federated learning communication cycles.

    Main Results:

    • Empirical demonstration of the proposed compression schemes' effectiveness on standard datasets.
    • Verification that the developed sparse models significantly reduce storage and bandwidth requirements compared to dense networks.
    • Outperformance of various state-of-the-art baseline schemes in terms of achieved accuracy and communication volume.

    Conclusions:

    • The proposed compression techniques successfully construct sparse deep neural network models that are efficient for federated learning.
    • These methods effectively mitigate the challenges of large model size and high communication costs in federated deep learning.
    • The developed schemes offer a promising approach for deploying deep learning models in resource-constrained federated environments.