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.
| Subjects: | Machine Learning (cs.LG); Statistics Theory (math.ST) |
| Cite as: | arXiv:2605.13283 [cs.LG] |
| (or arXiv:2605.13283v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13283 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuxuan Wang [view email]
[v1]
Wed, 13 May 2026 10:00:23 UTC (391 KB)