Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning

Jinke Ren; Yinghui He; Dingzhu Wen; Guanding Yu; Kaibin Huang; Dongning Guo

doi:10.1109/IEEECONF51394.2020.9443300

Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning

Jinke Ren, Yinghui He, Dingzhu Wen, Guanding Yu, Kaibin Huang, Dongning Guo

Electrical and Computer Engineering

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

4 Scopus citations

Abstract

This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.

Original language	English (US)
Title of host publication	Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	294-298
Number of pages	5
ISBN (Electronic)	9780738131269
DOIs	https://doi.org/10.1109/IEEECONF51394.2020.9443300
State	Published - Nov 1 2020
Event	54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States Duration: Nov 1 2020 → Nov 5 2020

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2020-November
ISSN (Print)	1058-6393

Conference

Conference	54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/Territory	United States
City	Pacific Grove
Period	11/1/20 → 11/5/20

Funding

VII. ACKNOWLEDGEMENT This work was supported by the National Science Foundation under Grants No. CCF-1910168 and CNS-2003098.

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF51394.2020.9443300

Cite this

Ren, J., He, Y., Wen, D., Yu, G., Huang, K., & Guo, D. (2020). Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning. In M. B. Matthews (Ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 (pp. 294-298). Article 9443300 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF51394.2020.9443300

Ren, Jinke ; He, Yinghui ; Wen, Dingzhu et al. / Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning. Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. editor / Michael B. Matthews. IEEE Computer Society, 2020. pp. 294-298 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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abstract = "This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.",

author = "Jinke Ren and Yinghui He and Dingzhu Wen and Guanding Yu and Kaibin Huang and Dongning Guo",

note = "Funding Information: VII. ACKNOWLEDGEMENT This work was supported by the National Science Foundation under Grants No. CCF-1910168 and CNS-2003098. Publisher Copyright: {\textcopyright} 2020 IEEE.; 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 ; Conference date: 01-11-2020 Through 05-11-2020",

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Ren, J, He, Y, Wen, D, Yu, G, Huang, K & Guo, D 2020, Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning. in MB Matthews (ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020., 9443300, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2020-November, IEEE Computer Society, pp. 294-298, 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020, Pacific Grove, United States, 11/1/20. https://doi.org/10.1109/IEEECONF51394.2020.9443300

Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning. / Ren, Jinke; He, Yinghui; Wen, Dingzhu et al.
Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. ed. / Michael B. Matthews. IEEE Computer Society, 2020. p. 294-298 9443300 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November).

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

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T1 - Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning

AU - Ren, Jinke

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N2 - This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.

AB - This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.

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Ren J, He Y, Wen D, Yu G, Huang K, Guo D. Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning. In Matthews MB, editor, Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. IEEE Computer Society. 2020. p. 294-298. 9443300. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF51394.2020.9443300

Importance- And Channel-Aware Scheduling in Cellular Federated Edge Learning

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