Lead federated neuromorphic learning for wireless edge artificial intelligence

Helin Yang; Kwok-Yan Lam; Liang Xiao; Zehui Xiong; Hao Hu; Dusit Niyato; H. Vincent Poor

doi:10.1038/s41467-022-32020-w

Nature Communications (Jul 2022)

Lead federated neuromorphic learning for wireless edge artificial intelligence

Helin Yang,
Kwok-Yan Lam,
Liang Xiao,
Zehui Xiong,
Hao Hu,
Dusit Niyato,
H. Vincent Poor

Affiliations

Helin Yang: Department of Information and Communication Engineering, School of Informatics, Xiamen University
Kwok-Yan Lam: Strategic Centre for Research in Privacy-Preserving Technologies and Systems, Nanyang Technological University
Liang Xiao: Department of Information and Communication Engineering, School of Informatics, Xiamen University
Zehui Xiong: Pillar of Information Systems Technology and Design, Singapore University of Technology and Design
Hao Hu: Department of Electrical and Electronic Engineering, Nanyang Technological University
Dusit Niyato: School of Computer Science and Engineering, Nanyang Technological University
H. Vincent Poor: Department of Electrical and Computer Engineering, Princeton University

DOI: https://doi.org/10.1038/s41467-022-32020-w
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

Read online

Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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