Fetching the paper…
Reading the bibliography…
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed.
Communication complexity of convex optimization
John N Tsitsiklis and Zhi-Quan Luo · 1987
Earlier work this paper cites.
Quantized incremental algorithms for distributed optimization
Michael G Rabbat and Robert D Nowak · 2005
Earlier work this paper cites.
Distributed subgradient methods and quantization effects
Angelia Nedic, Alex Olshevsky, Asuman Ozdaglar, and John N Tsitsiklis · 2008
Earlier work this paper cites.
Convex Analysis and Monotone Operator Theory in Hilbert Spaces
H. H. Bauschke and P. L. Combettes · 2011
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
Earlier work this paper cites.
A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
L. Condat · 2013
Earlier work this paper cites.
A splitting algorithm for dual monotone inclusions involving cocoercive operators
B. C. Vũ · 2013
Earlier work this paper cites.
On stochastic proximal gradient algorithms
Y. F. Atchade, G. Fort, and E. Moulines · 2014
Earlier work this paper cites.
First-order methods of smooth convex optimization with inexact oracle
Olivier Devolder, François Glineur, and Yurii Nesterov · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014
Earlier work this paper cites.
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Cited alongside, same era.
On perturbed proximal gradient algorithms
Y. F Atchadé, G. Fort, and E. Moulines · 2017
Cited alongside, same era.
A three-operator splitting scheme and its optimization applications
D. Davis and W. Yin · 2017
Cited alongside, same era.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
Cited alongside, same era.
Jianyu Wang and Gauri Joshi · 2018
Later among the works it cites.
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Debraj Basu, Deepesh Data, Can Karakus, and Suhas Diggavi · 2019
Closest in time.
Nested distributed gradient methods with adaptive quantized communication
Albert S Berahas, Charikleia Iakovidou, and Ermin Wei · 2019
Closest in time.
SGD: general analysis and improved rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Anima Anandkumar · 2018
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, and Ameet Talwalkar · 2018
Cited alongside, same era.
Accelerating the convergence rates of distributed subgradient methods with adaptive quantization
Thinh T Doan, Siva Theja Maguluri, and Justin Romberg · 2018
Cited alongside, same era.
Finite rate quantized distributed optimization with geometric convergence
Chang-Shen Lee, Nicolò Michelusi, and Gesualdo Scutari · 2018
Cited alongside, same era.
Quantized decentralized consensus optimization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, and Ramtin Pedarsani · 2018
Cited alongside, same era.
Sebastian U. Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
Cited alongside, same era.
Samuel Horváth, Chen-Yu Ho, Ľudovít Horvath, Atal Narayan Sahu, Marco Canini, and Peter Richtárik · 2019
Closest in time.
Stochastic Distributed Learning with Gradient Quantization and Variance Reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Sebastian Stich, and Peter Richtárik · 2019
Closest in time.
Gradient descent with compressed iterates
Ahmed Khaled and Peter Richtárik · 2019
Closest in time.
Decentralized stochastic optimization and gossip algorithms with compressed communication
Anastasia Koloskova, Sebastian U Stich, and Martin Jaggi · 2019
Closest in time.
Distributed learning with compressed gradient differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč, and Peter Richtárik · 2019
Closest in time.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2019
Closest in time.
Compressed distributed gradient descent: Communication-efficient consensus over networks
Xin Zhang, Jia Liu, Zhengyuan Zhu, and Elizabeth S Bentley · 2019
Closest in time.