Towards taming the resource and data heterogeneity in federated learning

Zheng Chai; Hannan Fayyaz; Zeshan Fayyaz; Ali Anwar; Yi Zhou; Nathalie Baracaldo; Heiko Ludwig; Yue Cheng

Towards taming the resource and data heterogeneity in federated learning

Zheng Chai
, Hannan Fayyaz
, Zeshan Fayyaz
, Ali Anwar
, Yi Zhou
, Nathalie Baracaldo
, Heiko Ludwig
, Yue Cheng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English (US)
Title of host publication	Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019
Publisher	USENIX Association
Pages	19-21
Number of pages	3
ISBN (Electronic)	9781939133007
State	Published - 2019
Externally published	Yes
Event	2019 USENIX Conference on Operational Machine Learning, OpML 2019 - Santa Clara, United States Duration: May 20 2019 → …

Publication series

Name	Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019

Conference

Conference	2019 USENIX Conference on Operational Machine Learning, OpML 2019
Country/Territory	United States
City	Santa Clara
Period	5/20/19 → …

Bibliographical note

Funding Information:
We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant.

Funding Information:
Acknowledgments We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant.

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Cite this

Chai, Z., Fayyaz, H., Fayyaz, Z., Anwar, A., Zhou, Y., Baracaldo, N., Ludwig, H., & Cheng, Y. (2019). Towards taming the resource and data heterogeneity in federated learning. In Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019 (pp. 19-21). (Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019). USENIX Association.

Towards taming the resource and data heterogeneity in federated learning. / Chai, Zheng; Fayyaz, Hannan; Fayyaz, Zeshan et al.
Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019. USENIX Association, 2019. p. 19-21 (Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chai, Z, Fayyaz, H, Fayyaz, Z, Anwar, A, Zhou, Y, Baracaldo, N, Ludwig, H & Cheng, Y 2019, Towards taming the resource and data heterogeneity in federated learning. in Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019. Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019, USENIX Association, pp. 19-21, 2019 USENIX Conference on Operational Machine Learning, OpML 2019, Santa Clara, United States, 5/20/19.

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title = "Towards taming the resource and data heterogeneity in federated learning",

author = "Zheng Chai and Hannan Fayyaz and Zeshan Fayyaz and Ali Anwar and Yi Zhou and Nathalie Baracaldo and Heiko Ludwig and Yue Cheng",

note = "Funding Information: We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant. Funding Information: Acknowledgments We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant.; 2019 USENIX Conference on Operational Machine Learning, OpML 2019 ; Conference date: 20-05-2019",

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series = "Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019",

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AU - Zhou, Yi

AU - Baracaldo, Nathalie

AU - Ludwig, Heiko

AU - Cheng, Yue

N1 - Funding Information: We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant. Funding Information: Acknowledgments We thank the reviewers for their feedback. This work is sponsored in part by George Mason University, an AWS Cloud Research Grant, and a Google Cloud Platform Research Grant.

PY - 2019

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M3 - Conference contribution

AN - SCOPUS:85075869009

T3 - Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019

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BT - Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019

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T2 - 2019 USENIX Conference on Operational Machine Learning, OpML 2019

Y2 - 20 May 2019

ER -

Towards taming the resource and data heterogeneity in federated learning

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Conference

Bibliographical note

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