Lead federated neuromorphic learning for wireless edge artificial intelligence
Abstract
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Journal
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- Nature Communications
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Nature Communications 13 (1), 2022-07-25
Springer Science and Business Media LLC
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Details 詳細情報について
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- CRID
- 1360861711907457024
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- ISSN
- 20411723
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- Data Source
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- Crossref