背景:近年来,工业物联网(IIoT)设备的激增导致各个领域的数据生成大幅增加,包括新生的6G网络。数字孪生(DTs),充当物理实体的虚拟副本,由于它们能够以经济有效的方式模拟和优化物理系统,因此在物联网领域中获得了普及。尽管如此,DTs的安全性及其产生的敏感数据的保护已成为最重要的问题。幸运的是,联邦学习(FL)系统已成为解决DT中数据隐私挑战的有前途的解决方案。尽管如此,用于训练目的的大量标记数据的必要获取构成了巨大的挑战,特别是在混合了真实和虚拟数据的数字孪生环境中。
目标:为了应对这一挑战,这项研究提出了一种创新的半监督FL(SSFL)框架,旨在通过战略利用伪标签来克服标记数据的稀缺性。
方法:具体来说,我们提出的SSFL算法,命名为SSFL-MBE,通过结合混合数据增强和贝叶斯估计一致性正则化损失,引入了一种新的方法,从而集成了鲁棒的增强技术来增强模型的泛化。此外,我们引入了贝叶斯估计的伪标签损失,利用先验概率知识来增强模型性能。我们的调查特别关注一个要求苛刻的场景,即标记和未标记的数据在不同的位置隔离。具体来说,服务器和各种客户端。
结果:对CIFAR-10和MNIST数据集进行的综合评估最终证明,我们提出的算法始终超越主流SSLL基线模型,表现出从0.5%到1.5%的模型性能增强。
结论:总体而言,这项工作有助于在DT授权的FL设置中开发更有效和安全的模型训练方法,这对于在支持6G的环境中部署IIoT至关重要。
BACKGROUND: In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data.
OBJECTIVE: To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels.
METHODS: Specifically, our proposed SSFL algorithm, named SSFL-MBE, introduces a novel approach by combining Mix data augmentation and Bayesian Estimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients.
RESULTS: Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%.
CONCLUSIONS: Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments.