Lead federated neuromorphic learning for wireless edge artificial intelligence

• المؤلفون الرئيسيون: Yang, Helin, Lam, Kwok-Yan, Xiao, Liang, Xiong, Zehui, Hu, Hao, Niyato, Dusit, Poor, H. Vincent
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Artificial Intelligence; Brain
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/167993

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...