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Related Experiment Video

Updated: Dec 4, 2025

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

876

Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning.

Jun Sun, Tianyi Chen, Georgios B Giannakis

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 23, 2020
    PubMed
    Summary

    This study introduces a novel approach for federated learning that significantly reduces communication costs. The Lazily Aggregated Quantized (LAQ) gradient method quantizes and selectively transmits gradients, improving efficiency.

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    Last Updated: Dec 4, 2025

    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

    876

    Area of Science:

    • Machine Learning
    • Distributed Systems
    • Optimization

    Background:

    • Federated learning enables collaborative model training without sharing raw data.
    • Communication overhead is a major bottleneck in federated learning systems.
    • Existing methods often struggle to balance communication efficiency and model accuracy.

    Purpose of the Study:

    • To develop a communication-efficient federated learning algorithm.
    • To reduce the number of transmitted bits and communication rounds in federated learning.
    • To achieve theoretical convergence guarantees while minimizing communication costs.

    Main Methods:

    • A novel distributed quantized gradient approach termed Lazily Aggregated Quantized (LAQ) gradient is proposed.
    • The method involves quantizing local gradients and adaptively skipping less informative communications.
    • The server aggregates quantized gradients to update the global model parameter.

    Main Results:

    • Theoretically, LAQ achieves linear convergence comparable to gradient descent in strongly convex settings.
    • Empirical results demonstrate significant reductions in communication costs (bits and rounds).
    • LAQ outperforms state-of-the-art gradient- and stochastic gradient-based algorithms in terms of communication efficiency.

    Conclusions:

    • The LAQ algorithm offers a substantial improvement in communication efficiency for federated learning.
    • This approach effectively addresses the communication bottleneck in distributed machine learning.
    • LAQ provides a practical solution for deploying federated learning in resource-constrained environments.