66 citations · 159 across the 21 of their papers we have counts for
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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…
Error Compensated Loopless SVRG, Quartz, and SDCA for Distributed Optimization
Xun Qian, Hanze Dong, Peter Richtárik +1
The communication of gradients is a key bottleneck in distributed training of large scale machine learning models. In order to reduce the communication cost, gradient compression (…
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-…
Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtárik
We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inher…