Details

Autor(en) / Beteiligte

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

Titel

Lead federated neuromorphic learning for wireless edge artificial intelligence

Ist Teil von

• Nature communications, 2022-07, Vol.13 (1), p.4269-12, Article 4269

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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.