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cs.LG2022★ 1 cited
Adaptive Compression for Communication-Efficient Distributed Training
Maksim Makarenko, Elnur Gasanov, Rustem Islamov +2
We propose Adaptive Compressed Gradient Descent (AdaCGD) - a novel optimization algorithm for communication-efficient training of supervised machine learning models with adaptive c…
cs.LG2022★ 3 cited
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation
Rustem Islamov, Xun Qian, Slavomír Hanzely +2
Despite their high computation and communication costs, Newton-type methods remain an appealing option for distributed training due to their robustness against ill-conditioned conv…