P. Watcharapichat et al. Ako: Decentralised deep learning with partial gradient exchange. In Proceedings of the Seventh ACM Symposium on Cloud Computing, pages 84--97. ACM, 2016.

Digital Library

Google Scholar

Cited By

View all

Sharma SSharma V(2024)IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine LearningWireless Personal Communications10.1007/s11277-024-10952-4134:2(1261-1281)Online publication date: 3-Apr-2024
https://doi.org/10.1007/s11277-024-10952-4
Wu ZWu XLong Y(2023)Joint Scheduling and Robust Aggregation for Federated Localization Over Unreliable Wireless D2D NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.323238120:3(3359-3379)Online publication date: Sep-2023
https://doi.org/10.1109/TNSM.2022.3232381
Liu LSong LChen XYu HSun G(2022)Accelerating model synchronization for distributed machine learning in an optical wide area networkJournal of Optical Communications and Networking10.1364/JOCN.46228614:10(852)Online publication date: 27-Sep-2022
https://doi.org/10.1364/JOCN.462286
Show More Cited By

Decentralized Distributed Deep Learning in Heterogeneous WAN Environments
1. Computer systems organization
  1. Architectures

Recommendations

RelaySum for decentralized deep learning on heterogeneous data
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems

In decentralized machine learning, workers compute model updates on their local data. Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network. This paradigm enables ...
Learning to Learn Multi-Party Learning : from Both Distributed and Decentralized Perspectives
Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning
Abstract
Decentralization is a central characteristic of biological motor control that allows for fast responses relying on local sensory information. In contrast, the current trend of Deep Reinforcement Learning (DRL) based approaches to motor ...
Graphical abstract

Display Omitted
Highlights
- Deep Reinforcement Learning in motor control is heavily affected by overfitting.

Comments

Information & Contributors

Information

Published In

SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

October 2018

546 pages

ISBN:9781450360111

DOI:10.1145/3267809

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 October 2018

Check for updates

Qualifiers

Poster
Research
Refereed limited

Funding Sources

National Science Foundation

Conference

SoCC '18

Sponsor:

SoCC '18: ACM Symposium on Cloud Computing

October 11 - 13, 2018

CA, Carlsbad, USA

Acceptance Rates

Overall Acceptance Rate 169 of 722 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
619
Total Downloads

Downloads (Last 12 months)62
Downloads (Last 6 weeks)12

Reflects downloads up to 12 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sharma SSharma V(2024)IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine LearningWireless Personal Communications10.1007/s11277-024-10952-4134:2(1261-1281)Online publication date: 3-Apr-2024
https://doi.org/10.1007/s11277-024-10952-4
Wu ZWu XLong Y(2023)Joint Scheduling and Robust Aggregation for Federated Localization Over Unreliable Wireless D2D NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.323238120:3(3359-3379)Online publication date: Sep-2023
https://doi.org/10.1109/TNSM.2022.3232381
Liu LSong LChen XYu HSun G(2022)Accelerating model synchronization for distributed machine learning in an optical wide area networkJournal of Optical Communications and Networking10.1364/JOCN.46228614:10(852)Online publication date: 27-Sep-2022
https://doi.org/10.1364/JOCN.462286
Yeo SBae MJeong MKwon OOh S(2022)Crossover‐SGD: A gossip‐based communication in distributed deep learning for alleviating large mini‐batch problem and enhancing scalabilityConcurrency and Computation: Practice and Experience10.1002/cpe.750835:15Online publication date: 19-Dec-2022
https://doi.org/10.1002/cpe.7508
Zhou HCai WLi ZYu HLiu LLuo LSun G(2021)TSEngine: Enable Efficient Communication Overlay in Distributed Machine Learning in WANsIEEE Transactions on Network and Service Management10.1109/TNSM.2021.310631518:4(4846-4859)Online publication date: Dec-2021
https://doi.org/10.1109/TNSM.2021.3106315
Anwar MGhafouri SGill SDoyle J(2021)Recommender System for Optimal Distributed Deep Learning in Cloud DatacentersWireless Personal Communications10.1007/s11277-021-08699-3127:2(1453-1477)Online publication date: 29-Jun-2021
https://doi.org/10.1007/s11277-021-08699-3
Luo QHe JZhuo YQian XLarus JCeze LStrauss K(2020)Prague: High-Performance Heterogeneity-Aware Asynchronous Decentralized TrainingProceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3373376.3378499(401-416)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.1145/3373376.3378499

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

RelaySum for decentralized deep learning on heterogeneous data

Learning to Learn Multi-Party Learning : from Both Distributed and Decentralized Perspectives

Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning

Comments

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Funding Sources

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

PDF

eReader

Login options

Full Access

Abstract

References

Cited By

Recommendations

RelaySum for decentralized deep learning on heterogeneous data

Learning to Learn Multi-Party Learning : from Both Distributed and Decentralized Perspectives

Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations