DRACO: Robust Distributed Training via Redundant Gradients

By Lingjiao Chen, Hongyi Wang, Zachary B. Charles, Dimitris Papailiopoulos. 2018

23 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Cong Xie
2. Hongyi Wang
3. Xinyang Cao
4. Dong Yin
5. Deepesh Data
6. Lingjiao Chen
7. Lifeng Lai
8. Dimitris Papailiopoulos

Citations in the corpus

• The Effect of Network Width on the Performance of Large-batch Training (2018, background): “Distributed implementations of stochastic optimization algorithms have become the standard in large-scale model training [1, 2, 3, 4, 5, 6, 7, 8, 9].”
• Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers (2019, methodology): “[25] propose DRACO algorithm that uses ideas from coding theory to determine which machines are under attack.”
• Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning (2018, background): “Byzantine-robust distributed learning Solving large scale learning problems in distributed systems has received much attention in recent years, where communication efficiency and Byzantine robustness are two important topics [58, 41, 70, 10, 15, 20].”