58 citations · 58 across the 3 of their papers we have counts for
5 papers · 1 filter
CSER: Communication-efficient SGD with Error Reset
Cong Xie, Shuai Zheng, Oluwasanmi Koyejo +3
The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks. We propose a novel SGD variant: Communication-efficient SGD with Err…
Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta +1
When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learnin…
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
Cong Xie, Sanmi Koyejo, Indranil Gupta
Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning. The Byzantine model captures workers that behave arbitrarily,…
SLSGD: Secure and Efficient Distributed On-device Machine Learning
Cong Xie, Sanmi Koyejo, Indranil Gupta
We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communica…
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generaliz…