10 citations · 78 across the 72 of their papers we have counts for
3 papers · 1 filter
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Xun Qian, Rustem Islamov, Mher Safaryan +1
Recent advances in distributed optimization have shown that Newton-type methods with proper communication compression mechanisms can guarantee fast local rates and low communicatio…
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Elnur Gasanov, Ahmed Khaled, Samuel Horváth +1
Federated Learning (FL) is an increasingly popular machine learning paradigm in which multiple nodes try to collaboratively learn under privacy, communication and multiple heteroge…
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques
Bokun Wang, Mher Safaryan, Peter Richtárik
To address the high communication costs of distributed machine learning, a large body of work has been devoted in recent years to designing various compression strategies, such as…