Efficient Byzantine-robust distributed inference with regularization: a trade-off between compression and adversary

DOI10.1016/j.ins.2024.121010MaRDI QIDQ6571196

Le Chang, Guang Yang, Xing-Cai Zhou, Shao-Gao Lv

Publication date: 11 July 2024

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

compressiondistributed learningadversarycommunication-efficientByzantine-robuststatistical error rate

Mathematics Subject Classification ID

Computer science (68-XX) Operations research, mathematical programming (90-XX)

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• Distributed testing and estimation under sparse high dimensional models
• The landscape of empirical risk for nonconvex losses
• Distributed secure state estimation for cyber-physical systems under sensor attacks
• Byzantine-resilient distributed state estimation: a min-switching approach
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Communication-Efficient Distributed Statistical Inference
• Fault-Tolerant Multi-Agent Optimization
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Efficient Byzantine-robust distributed inference with regularization: a trade-off between compression and adversary