FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK

Jie Ding; Eric Tramel; Anit Kumar Sahu; Shuang Wu; Salman Avestimehr; Tao Zhang

doi:10.1109/ICASSP43922.2022.9746925

FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK

Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang

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

1 Scopus citations

Abstract

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled “Frontiers of Federated Learning: Applications, Challenges, and Opportunities.” The outlook is categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.

Original language	English (US)
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8752-8756
Number of pages	5
ISBN (Electronic)	9781665405409
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9746925
State	Published - 2022
Externally published	Yes
Event	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: May 23 2022 → May 27 2022

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2022-May
ISSN (Print)	1520-6149

Conference

Conference	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/Territory	Singapore
City	Virtual, Online
Period	5/23/22 → 5/27/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE

Keywords

Distributed learning
nonstandard data

Publisher link

10.1109/ICASSP43922.2022.9746925

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Cite this

Ding, J., Tramel, E., Sahu, A. K., Wu, S., Avestimehr, S., & Zhang, T. (2022). FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings (pp. 8752-8756). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP43922.2022.9746925

FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES : AN OUTLOOK. / Ding, Jie; Tramel, Eric; Sahu, Anit Kumar et al.

2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 8752-8756 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May).

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

Ding, J, Tramel, E, Sahu, AK, Wu, S, Avestimehr, S & Zhang, T 2022, FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, Institute of Electrical and Electronics Engineers Inc., pp. 8752-8756, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 5/23/22. https://doi.org/10.1109/ICASSP43922.2022.9746925

Ding J, Tramel E, Sahu AK, Wu S, Avestimehr S, Zhang T. FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 8752-8756. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP43922.2022.9746925

Ding, Jie ; Tramel, Eric ; Sahu, Anit Kumar et al. / FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES : AN OUTLOOK. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 8752-8756 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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AB - Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled “Frontiers of Federated Learning: Applications, Challenges, and Opportunities.” The outlook is categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.

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FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

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