On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication-Reference-Cited by-同舟云学术

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On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication

Published:2020 Issue: Volume:68 Page:6101-6116
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Magnusson Sindri^ORCID,Shokri-Ghadikolaei Hossein^ORCID,Li Na^ORCID

Funder

Wallenberg AI, Autonomous Systems and Software Program

Knut och Alice Wallenbergs Stiftelse

Stiftelsen för Strategisk Forskning

Swedish Research Foundation

NSF CAREER

AFOSR YIP

ONR YIP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/8933520/09226092.pdf?arnumber=9226092

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3. Neuron Pruning-Based Federated Learning for Communication-Efficient Distributed Training;Lecture Notes in Computer Science;2024

4. Distributed Constrained Optimization Algorithm for Higher-Order Multi-Agent Systems;IEEE Transactions on Signal and Information Processing over Networks;2024

5. Distributed constrained optimization with periodic dynamic quantization;Automatica;2024-01

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