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Related Experiment Video

Updated: Jan 8, 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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Slack Federated Adversarial Training.

Jianing Zhu, Bo Han, Jiangchao Yao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 22, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Federated adversarial training can degrade accuracy due to increased data heterogeneity. Slack Federated Adversarial Training (SFAT) mitigates this by relaxing objectives and using weighted aggregation for improved robust accuracy in federated models.

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    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

    983

    Area of Science:

    • Machine Learning
    • Cybersecurity
    • Distributed Systems

    Background:

    • Federated learning (FL) and adversarial training (AT) are crucial for robust and private AI.
    • Combining FL and AT can lead to accuracy degradation in later training stages.
    • This degradation stems from adversarial data exacerbating client data heterogeneity.

    Purpose of the Study:

    • To address the accuracy degradation in federated adversarial training.
    • To propose a novel framework that combats intensified data heterogeneity.
    • To improve the robust accuracy of federated models under adversarial attacks.

    Main Methods:

    • Introduced an alpha-slack mechanism to relax the federated adversarial training objective.
    • Developed the Slack Federated Adversarial Training (SFAT) framework.
    • Proposed SFAT* with hierarchical aggregation for mixed standard/adversarial training clients.

    Main Results:

    • SFAT alleviates optimization bias through client-wise slack and weighted aggregation.
    • Theoretical analysis confirms the convergence of the relaxed learning objective.
    • Experimental validation on diverse datasets demonstrates effectiveness against various FL/AT methods.

    Conclusions:

    • The proposed SFAT framework effectively combats intensified data heterogeneity in federated adversarial training.
    • SFAT improves robust accuracy by mitigating optimization bias.
    • The methods are generalizable and effective across different training settings and datasets.