Byzantine-Robust Distributed Sparse Learning Revisited

Yuxuan Wang; Lixin Zhang; Kangqiang Li

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arXiv · 2605.13283

Byzantine-Robust Distributed Sparse Learning Revisited

Yuxuan Wang·Lixin Zhang·Kangqiang Li

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Abstract

We revisit Byzantine robust distributed estimation for high-dimensional sparse linear models. By combining local $\ell_1$-regularized robust estimation with robust aggregation at the server, the framework applies to pseudo-Huber regression, quantile regression, and sparse SVM. We show that the resulting estimators yield non-asymptotic guarantees and attain near-optimal statistical rates under mild conditions, while remaining communication-efficient. Simulations confirm strong robustness in estimation, support recovery and classification accuracy under various Byzantine attacks.

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Yuxuan Wang, Lixin Zhang, Kangqiang Li. 2026-05-13. Byzantine-Robust Distributed Sparse Learning Revisited. https://arxiv.org/abs/2605.13283

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