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) |
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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 | |
State | Published - 2022 |
Event | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, Singapore Duration: May 22 2022 → May 27 2022 |
Publication series
Name | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Conference
Conference | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 |
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Country/Territory | Singapore |
City | Hybrid |
Period | 5/22/22 → 5/27/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE
Keywords
- Distributed learning
- nonstandard data