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Related Experiment Video

Updated: Oct 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems.

Wuwei Huang1, Yang Yang1, Mingzhe Chen2

  • 1Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study optimizes federated learning (FL) network performance by integrating visible light communication (VLC) with radio frequency (RF) and employing model compression. This approach enhances communication efficiency and accuracy in machine learning applications.

Keywords:
federated learningmodel compressionvisible light communication

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Area of Science:

  • Wireless communication networks
  • Machine learning
  • Optimization theory

Background:

  • Federated learning (FL) enables decentralized model training but faces challenges with limited radio frequency (RF) resources, restricting user participation and increasing communication costs.
  • Efficient transmission of machine learning (ML) parameters is crucial for FL performance, yet current RF-based methods are resource-intensive.

Purpose of the Study:

  • To optimize network performance for federated learning (FL) deployment by enhancing communication efficiency and user selection.
  • To investigate the integration of visible light communication (VLC) as a supplement to RF to overcome resource limitations in FL networks.

Main Methods:

  • Proposed a hybrid RF and VLC system for FL, incorporating model compression techniques to reduce data transmission size.
  • Formulated a user selection and bandwidth allocation problem as an optimization task to minimize FL training loss.
  • Solved the optimization problem by separating it into user selection (traversal algorithm) and bandwidth allocation (numerical method) subproblems, solved iteratively.

Main Results:

  • The proposed FL algorithm significantly improves object recognition accuracy by up to 16.7% compared to conventional RF-only methods.
  • Achieved an increase in the number of selected users by up to 68.7% through optimized resource allocation and user selection.
  • Demonstrated enhanced communication efficiency by reducing the resources needed for FL parameter transmission.

Conclusions:

  • The integration of VLC and model compression effectively optimizes wireless network performance for FL.
  • The proposed optimization strategy enhances FL model accuracy and expands user participation in resource-constrained environments.
  • This hybrid approach offers a promising solution for efficient and scalable federated learning deployments.