Corpus ID: 198968245

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

@inproceedings{Rajput2019DETOXAR,
  title={DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation},
  author={Shashank Rajput and H. Wang and Zachary Charles and Dimitris Papailiopoulos},
  booktitle={NeurIPS},
  year={2019}
}

Shashank Rajput, H. Wang, +1 author Dimitris Papailiopoulos

Published in NeurIPS 2019

Computer Science, Mathematics

To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness guarantees. Other methods have instead used redundancy to guarantee robustness, but can only tolerate limited number of Byzantine failures. In this work, we present DETOX, a… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed