Lead federated neuromorphic learning for wireless edge artificial intelligence

• Main Authors: Yang, Helin, Lam, Kwok-Yan, Xiao, Liang, Xiong, Zehui, Hu, Hao, Niyato, Dusit, Poor, H. Vincent
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Artificial Intelligence; Brain
• Online Access: https://hdl.handle.net/10356/167993
• Institution: Nanyang Technological University

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...