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

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish (US)
Article number4269
JournalNature communications
Issue number1
StatePublished - Dec 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy
  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology


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