Byzantine-Robust Distributed Sparse Learning Revisited
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Abstract (arXiv)
We revisit Byzantine robust distributed estimation for high-dimensional sparse linear models. By combining local ℓ1-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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References · 9
- 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
- 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 132
- 2016Efficient Distributed Learning with Sparsitycited by 38
- 2019Communication-Efficient Accurate Statistical Estimationcited by 32
- 2020Smoothed Quantile Regression with Large-Scale Inferencecited by 23
- 2023A Huber Loss Minimization Approach to Byzantine Robust Federated Learningcited by 16
- 2021Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inferencecited by 11
- 2014Sparse Quantile Huber Regression for Efficient and Robust Estimationcited by 2
- 2021Distributed Adaptive Huber Regressioncited by 2
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