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MARINA: Faster Non-Convex Distributed Learning with Compression
International Conference on Machine Learning (ICML), 2021
15 February 2021
Eduard A. Gorbunov
Konstantin Burlachenko
Zhize Li
Peter Richtárik
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Papers citing
"MARINA: Faster Non-Convex Distributed Learning with Compression"
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Aleksandr Beznosikov
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MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes
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Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
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Distributed Sign Momentum with Local Steps for Training Transformers
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Ding Zhou
Cong Xie
An Xu
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Xin Liu
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H. Vincent Poor
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248
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Grigory Malinovsky
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243
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391
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Optimal Data Splitting in Distributed Optimization for Machine Learning
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Gleb Molodtsov
Aleksandr Beznosikov
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270
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Activations and Gradients Compression for Model-Parallel Training
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Mikhail Rudakov
Aleksandr Beznosikov
Yaroslav Kholodov
Alexander Gasnikov
393
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15 Jan 2024
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
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Maroun Touma
Yang Jiao
Jianwei Huang
FedML
307
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Abdulmajeed Alrowithi
Fahad Ali Albalawi
Peter Richtárik
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267
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04 Dec 2023
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal
Kaja Gruntkowska
Nikita Fedin
Eduard A. Gorbunov
Peter Richtárik
350
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Hanzheng Zhao
Cong Fang
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Liwei Wang
Zhouchen Lin
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215
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23 Sep 2023
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
IEEE Transactions on Mobile Computing (IEEE TMC), 2023
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Yuzhu Mao
Zhenpeng Shi
Yang Liu
Tian-Shing Lan
Wenbo Ding
Xiaoping Zhang
352
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01 Aug 2023
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Peter Richtárik
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Francesco Pase
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Sanmi Koyejo
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M. Zorzi
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182
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Error Feedback Shines when Features are Rare
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Elnur Gasanov
Konstantin Burlachenko
204
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Momentum Provably Improves Error Feedback!
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Ilyas Fatkhullin
Alexander Tyurin
Peter Richtárik
315
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24 May 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression
IEEE Journal on Selected Topics in Signal Processing (JSTSP), 2023
Boyue Li
Yuejie Chi
178
17
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17 May 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
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Xinmeng Huang
Yiming Chen
W. Yin
Kun Yuan
416
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12 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
International Conference on Machine Learning (ICML), 2023
Aleksandr Beznosikov
David Dobre
Gauthier Gidel
239
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23 Apr 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Avetik G. Karagulyan
Peter Richtárik
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254
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08 Mar 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
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Aleksandr Beznosikov
Martin Takáč
Alexander Gasnikov
316
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15 Feb 2023
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning
International Symposium on Information Theory (ISIT), 2023
Chanho Park
Namyoon Lee
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164
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DoCoFL: Downlink Compression for Cross-Device Federated Learning
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S. Vargaftik
Y. Ben-Itzhak
Kfir Y. Levy
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307
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01 Feb 2023
Federated Learning with Flexible Control
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Maroun Touma
Jake B. Perazzone
Mingyue Ji
Kevin S. Chan
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234
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210
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Coresets for Vertical Federated Learning: Regularized Linear Regression and
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Lingxiao Huang
Zhize Li
Jialin Sun
Haoyu Zhao
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215
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EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression
International Conference on Machine Learning (ICML), 2022
Kaja Gruntkowska
Alexander Tyurin
Peter Richtárik
389
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30 Sep 2022
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Sara Babakniya
Souvik Kundu
Saurav Prakash
Yue Niu
Salman Avestimehr
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274
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27 Aug 2022
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization
Journal of machine learning research (JMLR), 2022
Zhize Li
Jian Li
283
9
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22 Aug 2022
Fast Heterogeneous Federated Learning with Hybrid Client Selection
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Guangyuan Shen
D. Gao
Duanxiao Song
Libin Yang
Xukai Zhou
Shirui Pan
W. Lou
Fang Zhou
FedML
370
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10 Aug 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox
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Abdurakhmon Sadiev
D. Kovalev
Peter Richtárik
294
23
0
08 Jul 2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
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Zhize Li
Haoyu Zhao
Boyue Li
Yuejie Chi
FedML
250
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20 Jun 2022
Communication-Efficient Federated Learning With Data and Client Heterogeneity
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Giorgi Nadiradze
Dan Alistarh
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493
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Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities
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Alexander Gasnikov
250
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Anchor Sampling for Federated Learning with Partial Client Participation
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Song Guo
Zhihao Qu
Shiqi He
Ziming Liu
Jing Gao
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242
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Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
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Xinmeng Huang
Yiming Chen
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Kun Yuan
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Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation
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259
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Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees
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Binhang Yuan
Luka Rimanic
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Tri Dao
Beidi Chen
Christopher Ré
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Adil Salim
Peter Richtárik
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Samuel Horváth
Peter Richtárik
Gauthier Gidel
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298
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Ran Ben-Basat
S. Vargaftik
Amit Portnoy
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Y. Ben-Itzhak
Michael Mitzenmacher
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341
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26 May 2022
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