Lead federated neuromorphic learning for wireless edge artificial intelligence

Helin Yang, Kwok-Yan Lam (), Liang Xiao, Zehui Xiong, Hao Hu, Dusit Niyato and H. Vincent Poor
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Helin Yang: Xiamen University
Kwok-Yan Lam: Nanyang Technological University
Liang Xiao: Xiamen University
Zehui Xiong: Singapore University of Technology and Design
Hao Hu: Nanyang Technological University
Dusit Niyato: Nanyang Technological University
H. Vincent Poor: Princeton University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract 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 a decentralized energy-efficient brain-inspired computing method based on spiking neural networks. The proposed technique will enable edge devices to exploit brain-like biophysiological structure to collaboratively train a global model while helping preserve privacy. Experimental results show that, under the situation of uneven dataset distribution among edge devices, LFNL achieves a comparable recognition accuracy to existing edge AI techniques, while substantially reducing data traffic by >3.5× and computational latency by >2.0×. Furthermore, LFNL significantly reduces energy consumption by >4.5× compared to standard federated learning with a slight accuracy loss up to 1.5%. Therefore, the proposed LFNL can facilitate the development of brain-inspired computing and edge AI.

Date: 2022
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DOI: 10.1038/s41467-022-32020-w

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