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Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks.

Guanqiang Zhou, Ping Xu, Yue Wang

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
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    This study introduces a novel algorithm for distributed learning, enhancing model robustness against both data distribution shifts and Byzantine attacks. The research reveals a critical convergence breakpoint for norm-based screening (NBS) algorithms at 1/3 Byzantine nodes.

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    Area of Science:

    • Distributed learning systems
    • Machine learning robustness
    • Optimization theory

    Background:

    • Distributed learning faces threats from distributional shifts causing poor out-of-sample performance.
    • Byzantine attacks on working nodes can invalidate distributed learning results.
    • Addressing both distributional shifts and Byzantine attacks simultaneously presents significant challenges.

    Purpose of the Study:

    • To propose a new research direction for jointly addressing distributional shifts and Byzantine attacks in distributed learning.
    • To design a novel algorithm that provides both distributional robustness and Byzantine robustness.
    • To analyze the convergence properties and robustness of the proposed algorithm.

    Main Methods:

    • The study integrates distributionally robust optimization (DRO) with norm-based screening (NBS), a robust aggregation scheme.
    • Convergence proofs are provided for nonconvex, convex, and strongly convex learning models.
    • Theoretical analysis investigates the limitations of NBS regarding the percentage of Byzantine nodes.

    Main Results:

    • A new algorithm is developed that achieves both distributional and Byzantine robustness in distributed learning.
    • Convergence behaviors and endurability against Byzantine attacks are analyzed for various model types.
    • It is demonstrated that NBS-based algorithms, including the proposed one, fail to converge when Byzantine nodes constitute 1/3 or more of the total nodes, contrary to the common belief of 1/2.

    Conclusions:

    • The proposed algorithm effectively tackles both distributional shifts and Byzantine attacks in distributed learning.
    • The findings clarify the convergence breakpoint for NBS algorithms, establishing a new theoretical understanding.
    • This work is the first to simultaneously address distributional shifts and Byzantine attacks in distributed learning systems.