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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy.

Yanan Li, Shusen Yang, Xuebin Ren

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    We introduce new algorithms for asynchronous federated learning (AFL) with differential privacy (DP). Our methods enhance model accuracy and convergence speed while maintaining strong privacy guarantees for decentralized AI systems.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Cybersecurity

    Background:

    • Federated learning (FL) combined with differential privacy (DP) offers robust privacy protection.
    • Synchronous FL suffers from inefficiency due to device heterogeneity (straggler effect).
    • Asynchronous FL (AFL) mitigates the straggler effect but lacks comprehensive study, especially with DP.

    Purpose of the Study:

    • To address the challenge of utility optimization in DP-enhanced AFL.
    • To develop theoretically motivated, multi-stage adaptive private algorithms for DP-AFL.
    • To improve the trade-off between model utility and privacy in asynchronous federated learning.

    Main Methods:

    • Development of two DP-enhanced AFL frameworks considering universal factors for diverse adversary models.
    • Theoretical analysis of AFL model convergence to enable adaptive DP with high utility.
    • Implementation and evaluation of proposed algorithms across various training models and benchmark datasets.

    Main Results:

    • Proposed algorithms demonstrate superior performance compared to state-of-the-art methods.
    • Achieved up to 24% improvement in test accuracy for the same level of privacy loss.
    • Exhibited faster convergence rates in experimental evaluations.

    Conclusions:

    • The developed frameworks offer an analytical approach for private AFL.
    • The algorithms effectively balance model utility and privacy in DP-enhanced AFL.
    • The proposed methods are adaptable to a wider range of complex FL application scenarios.