Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3477114.3488760acmconferencesArticle/Chapter ViewAbstractPublication PagessospConference Proceedingsconference-collections
research-article

FedScale: Benchmarking Model and System Performance of Federated Learning

Published: 25 October 2021 Publication History
First page of PDF

References

[1]
AI Benchmark: All About Deep Learning on Smartphones. http://aibenchmark.com/ranking_deeplearning_detailed.html.
[2]
Fox Go Dataset. https://github.com/featurecat/go-dataset.
[3]
Google Open Images Dataset. https://storage.googleapis.com/openimages/web/index.html.
[4]
MobiPerf. https://www.measurementlab.net/tests/mobiperf/.
[5]
PySyft. https://github.com/OpenMined/PySyft.
[6]
Reddit Comment Data. https://files.pushshift.io/reddit/comments/.
[7]
Taobao Dataset. https://tianchi.aliyun.com/dataset/dataDetail?dataId=56&lang=en-us.
[8]
Keith Bonawitz, Hubert Eichner, and et al. Towards federated learning at scale: System design. In MLSys, 2019.
[9]
Sebastian Caldas, Sai Meher, Karthik Duddu, and et al. Leaf: A benchmark for federated settings. NeurIPS' Workshop, 2019.
[10]
David F. Fouhey, Weicheng Kuo, Alexei A. Efros, and Jitendra Malik. From lifestyle vlogs to everyday interactions. In CVPR, 2018.
[11]
Robin C. Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. In NeurIPS, 2017.
[12]
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. An efficient framework for clustered federated learning. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020.
[13]
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. Practical and private (deep) learning without sampling or shuffling. In arxiv.org/abs/2103.00039, 2021.
[14]
Peter Kairouz, H. Brendan McMahan, and et al. Advances and open problems in federated learning. In Foundations and TrendsÂő in Machine Learning, 2021.
[15]
Philipp Koehn. Europarl: A Parallel Corpus for Statistical Machine Translation. In Conference Proceedings: the tenth Machine Translation Summit, 2005.
[16]
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise AgÃijera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, 2017.
[17]
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In CVPR, 2018.
[18]
Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. In ECCV, 2016.
[19]
Chengxu Yang, Qipeng Wang, and et al. Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. In WWW, 2021.
[20]
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. Applied federated learning: Improving Google keyboard query suggestions. In arxiv.org/abs/1812.02903, 2018.
[21]
Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu. Libritts: A corpus derived from librispeech for text-to-speech. arXiv preprint arXiv:1904.02882, 2019.

Cited By

View all
  • (2024)Federated learning–based global road damage detectionComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1318639:14(2223-2238)Online publication date: 5-Mar-2024
  • (2024)Mixing Gradients in Neural Networks as a Strategy to Enhance Privacy in Federated Learning2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00391(3944-3953)Online publication date: 3-Jan-2024
  • (2024) Fed-ensemble : Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification IEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326963921:3(2792-2803)Online publication date: Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ResilientFL '21: Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning
October 2021
22 pages
ISBN:9781450387088
DOI:10.1145/3477114
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2021

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

SOSP '21
Sponsor:

Upcoming Conference

SOSP '24

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)42
  • Downloads (Last 6 weeks)4
Reflects downloads up to 01 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Federated learning–based global road damage detectionComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1318639:14(2223-2238)Online publication date: 5-Mar-2024
  • (2024)Mixing Gradients in Neural Networks as a Strategy to Enhance Privacy in Federated Learning2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00391(3944-3953)Online publication date: 3-Jan-2024
  • (2024) Fed-ensemble : Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification IEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326963921:3(2792-2803)Online publication date: Jul-2024
  • (2024)Towards Building The Federatedgpt: Federated Instruction TuningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447454(6915-6919)Online publication date: 14-Apr-2024
  • (2024)Federated learning energy saving through client selectionPervasive and Mobile Computing10.1016/j.pmcj.2024.101948103(101948)Online publication date: Oct-2024
  • (2024)A federated learning architecture for secure and private neuroimaging analysisPatterns10.1016/j.patter.2024.1010315:8(101031)Online publication date: Aug-2024
  • (2024)WBSP: Addressing stragglers in distributed machine learning with worker-busy synchronous parallelParallel Computing10.1016/j.parco.2024.103092121(103092)Online publication date: Sep-2024
  • (2024)A review of privacy-preserving research on federated graph neural networksNeurocomputing10.1016/j.neucom.2024.128166600(128166)Online publication date: Oct-2024
  • (2024)Recent advances on federated learning: A systematic surveyNeurocomputing10.1016/j.neucom.2024.128019597(128019)Online publication date: Sep-2024
  • (2024)Marvel: Towards Efficient Federated Learning on IoT DevicesComputer Networks10.1016/j.comnet.2024.110375245(110375)Online publication date: May-2024
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media