半监督的联合学习为数字双胞胎6G支持的IIoT:贝叶斯估计方法
Yuanhang Qi1, M Shamim Hossain2
1School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China.
Journal of advanced research
|February 28, 2024
概括
本研究介绍了一种半监督联合学习 (SSFL) 框架,使用伪标签安全训练数字双胞胎 (DTs). SSFL-MBE算法提高了6G网络中工业物联网 (IIoT) 的模型性能.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 工业物联网 (IIoT) 和6G网络产生了大量数据,增加了数字双胞胎 (DT) 的使用.
- 在数据技术中保护敏感数据至关重要.
- 联合学习 (FL) 提供隐私,但需要大量的标记数据,这在DT环境中是一个挑战.
研究的目的:
- 提出半监督联合学习 (SSFL) 框架,以解决DTs中标记数据的稀缺问题.
- 为了提高DT授权的FL设置模型培训的安全性和效率.
主要方法:
- 开发了SSFL-MBE算法,结合了混合数据增强和贝叶斯估计一致性规范化.
- 整合了贝叶斯估计的伪标签损失以利用概率知识.
- 专注于服务器和客户端之间分离的标记和未标记数据的场景.
主要成果:
- 根据CIFAR-10和MNIST数据集进行评估.
- 拟议的SSFL-MBE算法表现优于主流SSFL基线模型.
- 实现了0.5%至1.5%的模型性能增强.
结论:
- 为具有DT授权的FL提供高效和安全的模型培训做出贡献.
- 对于在支持6G的环境中部署工业物联网 (IIoT) 是至关重要的.
- 在复杂的数字双胞胎生态系统中推进保护隐私的机器学习.
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