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Related Experiment Video

Updated: Jan 10, 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

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Utility-Leakage Trade-Off for Federated Representation Learning.

Yuchen Liu1, Onur Günlü2,3, Yuanming Shi1

  • 1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.

Entropy (Basel, Switzerland)
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Federated representation learning (FRL) offers privacy benefits but risks sensitive data leakage. This study introduces a method to protect specific sensitive information in FRL, balancing utility and privacy effectively.

Keywords:
differential privacyfederated learningutility

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Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

1000

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Privacy

Background:

  • Federated representation learning (FRL) enables decentralized data analysis without raw data sharing.
  • Existing methods like differential privacy (DP) offer general protection but can degrade performance.
  • A critical need exists to protect specific sensitive information while preserving utility.

Purpose of the Study:

  • To investigate information-theoretic protection for sensitive attributes in FRL.
  • To develop a method that balances utility and sensitive information leakage.
  • To provide a tunable privacy-utility trade-off mechanism.

Main Methods:

  • Utilizing mutual information to quantify utility and sensitive information leakage.
  • Proposing a novel FRL method incorporating local DP.
  • Maximizing utility under a constraint on sensitive information leakage (less than ϵ).

Main Results:

  • The proposed scheme achieves superior utility-leakage trade-offs compared to baseline methods.
  • The method effectively protects specific sensitive information (e.g., race).
  • Controlling noise levels in local DP allows adjustable privacy-utility trade-offs.

Conclusions:

  • The developed method offers a targeted approach to privacy in FRL.
  • This technique enhances data utility while mitigating specific privacy risks.
  • The tunable nature of the method provides flexibility for various applications.