Lead federated neuromorphic learning for wireless edge artificial intelligence

Lead federated neuromorphic learning for wireless edge artificial intelligence

In order to realize the full potential of wireless edge artificial intelligence (AI), very large and diverse datasets will often be required for energy-demanding model training on resource-constrained edge devices. This paper proposes a lead federated neuromorphic learning (LFNL) technique, which is...

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Bibliographic Details
Main Authors: Yang, Helin, Lam, Kwok-Yan, Xiao, Liang, Xiong, Zehui, Hu, Hao, Niyato, Dusit, Poor, H. Vincent
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Artificial Intelligence
Brain
Online Access:https://hdl.handle.net/10356/167993
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Institution: Nanyang Technological University
Language: English

Internet

https://hdl.handle.net/10356/167993

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