434 citations · 851 across the 20 of their papers we have counts for
5 papers · 1 filter
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,…
Zeno++: Robust Fully Asynchronous SGD
Cong Xie, Sanmi Koyejo, Indranil Gupta
We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ rem…
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…