3 citations · 10 across the 18 of their papers we have counts for
4 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…
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…
FedNL: Making Newton-Type Methods Applicable to Federated Learning
Mher Safaryan, Rustem Islamov, Xun Qian +1
Inspired by recent work of Islamov et al (2021), we propose a family of Federated Newton Learn (FedNL) methods, which we believe is a marked step in the direction of making second-…
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Mher Safaryan, Filip Hanzely, Peter Richtárik
Large scale distributed optimization has become the default tool for the training of supervised machine learning models with a large number of parameters and training data. Recent…