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Proximal SCOPE for Distributed Sparse Learning: Better Data Partition
Implies Faster Convergence Rate

v1v2 (latest)

Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate

Neural Information Processing Systems (NeurIPS), 2018

15 March 2018

ArXiv (abs)PDF HTML

Papers citing "Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate"

6 / 6 papers shown

On the Optimal Batch Size for Byzantine-Robust Distributed Learning

On the Optimal Batch Size for Byzantine-Robust Distributed Learning

299

1

0

23 May 2023

FedREP: A Byzantine-Robust, Communication-Efficient and
Privacy-Preserving Framework for Federated Learning

FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning

295

6

0

09 Mar 2023

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear
Regression

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

337

1

0

16 Sep 2022

Buffered Asynchronous SGD for Byzantine Learning

Buffered Asynchronous SGD for Byzantine LearningJournal of machine learning research (JMLR), 2020

272

6

0

02 Mar 2020

Global Momentum Compression for Sparse Communication in Distributed
Learning

Global Momentum Compression for Sparse Communication in Distributed Learning

384

1

0

30 May 2019

Convergence of Distributed Stochastic Variance Reduced Methods without
Sampling Extra Data

Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra DataIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019

345

29

0

29 May 2019

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