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

NeurIPS 2019 Shashank RajputHongyi WangZachary CharlesDimitris Papailiopoulos

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... (read more)

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