超可靠和低延迟的无线分层联合学习:性能分析
Haonan Zhang1,2,3, Peng Xu2,4, Bin Dai1,2,3
1School of Information Science and Technology, Southwest JiaoTong University, Chengdu 611756, China.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了一种安全的有限块长度方法,用于超可靠的低延迟通信无线层次联合学习 (URLLC-WHFL). 该方法增强了物理层的安全性,而不会影响学习性能,即使有不完美的窃听者通道信息.
科学领域:
- 无线通信系统无线通信系统
- 机器学习安全性 机器学习安全性
- 信息理论是信息理论.
背景情况:
- 无线层次联合学习 (WHFL) 加快了模型培训,但面临窃听风险.
- 超可靠和低延迟通信 (URLLC) 对5G/6G至关重要,需要安全实施.
- 物理层安全 (PLS) 对于保护无线通信至关重要.
研究的目的:
- 为多天线URLLC-WHFL提出一个安全的有限块长度 (FBL) 方法.
- 在拟议方案中分析隐私,公用事业和PLS之间的权衡.
- 在URLLC环境中解决WHFL的窃听漏洞.
主要方法:
- 为多天线URLLC-WHFL开发一个安全的有限块长度 (FBL) 方法.
- 描述隐私,实用性和PLS之间的关系.
- 在不同间通道状态信息 (CSI) 条件下评估方案性能的模拟.
主要成果:
- 拟议的FBL方法实现了近乎完美的保密性.
- 学习表现不受安全措施的影响.
- 该计划证明了对不完美的窃听者CSI的稳定性.
结论:
- 为URLLC-WHFL.提供了一种新的安全FBL方法.
- 这种方法有效地平衡了安全性和学习效率.
- 这项工作为面对PLS挑战的安全URLLC-WHFL系统提供了可行的解决方案.
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