Self-Competence Learning for Bandwidth Slicing and Confidential Computing Requirement

Abstract

This work proposes a Self-Competence Learning bandwidth slicing method (SCL) to determine the quantity and position of each slice’s spectrum resources considering the channel diversity and varying bandwidth requirements. SCL obtains high throughput in the following aspects: (1) SCL provides artificial intelligence (AI) based functionality to determine the quantity and position of the spectrum resource, (2) SCL can be trained solely by self-competence without any assistance or labeling by manual operations, and (3) SCL provides confidentiality without compromising on training performance in a collaborative learning environment. Simulation results demonstrate SCL improves the 10% overall throughput and saves up 25% bandwidth resource for AI model merging.

Keywords

• Channel allocation
• confidential computing
• federal learning
• network slicing