Aerial Access Networks for Federated Learning: Applications and Challenges
- Authors
- Pham, Quoc-Viet; Zeng, Ming; Huynh-The, Thien; Han, Zhu; Hwang, Won-Joo
- Issue Date
- Jul-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Multi-access edge computing; Collaborative work
- Citation
- IEEE NETWORK, v.36, no.3, pp 159 - 166
- Pages
- 8
- Journal Title
- IEEE NETWORK
- Volume
- 36
- Number
- 3
- Start Page
- 159
- End Page
- 166
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28382
- DOI
- 10.1109/MNET.013.2100311
- ISSN
- 0890-8044
1558-156X
- Abstract
- Aerial access networks (AANs) and mobile edge computing (MEC) have been considered as key enablers of future networks. In this article, we investigate the application of MEC-empowered AANs (also known as aerial computing) for federated learning (FL), a promising technology for providing private and distributed solutions to mobile edge networks. We first introduce the fundamentals of AANs and FL, and illustrate the potential benefits of aerial FL networks. On this basis, we present important applications of AANs for FL. It is shown that distinctive characteristics such as flexible deployment and high mobility, when exploited cleverly, can provide various benefits for FL-enabled networks. Finally, major challenges and potential directions are highlighted.
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