58 citations · 59 across the 2 of their papers we have counts for
10 papers
Compressed Communication for Distributed Training: Adaptive Methods and System
Yuchen Zhong, Cong Xie, Shuai Zheng +1
Communication overhead severely hinders the scalability of distributed machine learning systems. Recently, there has been a growing interest in using gradient compression to reduce…
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…
Asynchronous Federated Optimization
Cong Xie, Sanmi Koyejo, Indranil Gupta
Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We…