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Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Enhancing Data Privacy in Human Factors Studies with Federated Learning.

Bingyi Su1, Liwei Qing1, Lu Lu1

  • 1North Carolina State University, USA.

Human Factors
|June 6, 2025
PubMed
Summary
This summary is machine-generated.

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Federated learning offers a privacy-preserving alternative for machine learning in human factors research. This approach achieves accuracy comparable to centralized methods while protecting sensitive human data.

Area of Science:

  • Human Factors and Ergonomics
  • Machine Learning
  • Data Privacy

Background:

  • Machine learning is transforming human factors research but faces privacy challenges with sensitive data.
  • Centralized machine learning models raise significant data privacy concerns.
  • Federated learning (FL) is proposed to address these privacy issues.

Purpose of the Study:

  • Develop and evaluate a privacy-preserving federated learning framework.
  • Assess FL efficacy in classifying mental stress during human-robot collaboration.
  • Assess FL efficacy in recognizing human activities during manual material handling.

Main Methods:

  • Constructed classifiers using both centralized and federated learning approaches.
  • Employed support vector machines for mental stress classification.
Keywords:
data privacyfederated learninghuman activity recognitionhuman factorsmental stress detection

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Last Updated: Jan 18, 2026

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

Published on: December 6, 2024

1.0K
  • Utilized a deep neural network (LSTM-CNN) for human activity recognition.

    • Main Results:

    • Federated learning models demonstrated accuracy comparable to centralized methods.
    • Performance differences between federated and centralized models were minimal (under 2.7%).
    • Federated learning effectively protected sensitive human data.

    Conclusions:

    • Federated learning is a viable alternative to traditional machine learning for human factors applications.
    • FL provides comparable accuracy with enhanced data privacy.
    • This research advances privacy-preserving methods for sensitive human-subject data.