# rTop-k: A Statistical Estimation Approach to Distributed SGD

@article{Barnes2020rTopkAS, title={rTop-k: A Statistical Estimation Approach to Distributed SGD}, author={Leighton Pate Barnes and Huseyin A. Inan and Berivan Isik and Ayfer {\"O}zg{\"u}r}, journal={IEEE Journal on Selected Areas in Information Theory}, year={2020}, volume={1}, pages={897-907} }

The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in techniques that reduce the communication cost of distributed Stochastic Gradient Descent (SGD), with gradient sparsification techniques such as top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and random-<inline…

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