Albogami Nasser Nammas
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Faculty of Tourism, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2025 Feb 3;15(1):4041. doi: 10.1038/s41598-025-88163-5.
In the present scenario, the Internet of Things (IoT) and edge computing technologies have been developing rapidly, foremost to the development of new tasks in security and privacy. Personal information and privacy leakage have become the main concerns in IoT edge computing surroundings. The promptly developing IoT-connected devices below an integrated Machine Learning (ML) method might threaten data confidentiality. The standard centralized ML-assisted methods have been challenging because they require vast numbers of data in a vital unit. Due to the rising distribution of information in many systems of linked devices, decentralized ML solutions have been required. Federated learning (FL) was proposed as an optimal solution to discover these privacy issues. Still, the heterogeneity of systems in IoT edge computing environments poses an essential task when executing FL. Therefore, this paper develops an Intelligent Deep Federated Learning Model for Enhancing Security (IDFLM-ES) approach in the IoT-enabled edge-computing environment. The presented IDFLM-ES approach aims to identify unwanted intrusions to certify the safety of the IoT environment. To accomplish this, the IDFLM-ES technique introduces a federated hybrid deep belief network (FHDBN) model using FL on time series data produced by the IoT edge devices. Besides, the IDFLM-ES technique uses data normalization and golden jackal optimization (GJO) based feature selection as a pre-processing step. Besides, the IDFLM-ES technique learns the individual and distributed feature representation over distributed databases to enhance model convergence for quick learning. Finally, the dung beetle optimizer (DBO) model is utilized to choose the effectual hyperparameter of the FHDBN model. The simulation value of the IDFLM-ES methodology is verified using a benchmark database. The experimental validation of the IDFLM-ES methodology portrayed a superior accuracy value of 98.24% compared to other models.
在当前情况下，物联网（IoT）和边缘计算技术发展迅速，这主要是由于安全和隐私方面新任务的发展。个人信息和隐私泄露已成为物联网边缘计算环境中的主要问题。在集成机器学习（ML）方法下迅速发展的物联网连接设备可能会威胁数据机密性。标准的集中式ML辅助方法一直具有挑战性，因为它们在关键单元中需要大量数据。由于许多连接设备系统中信息分布的增加，需要分散式ML解决方案。联邦学习（FL）被提出作为解决这些隐私问题的最佳方案。然而，物联网边缘计算环境中系统的异质性在执行FL时带来了一项重要任务。因此，本文在支持物联网的边缘计算环境中开发了一种用于增强安全性的智能深度联邦学习模型（IDFLM-ES）方法。所提出的IDFLM-ES方法旨在识别不必要的入侵，以确保物联网环境的安全。为了实现这一目标，IDFLM-ES技术在物联网边缘设备产生的时间序列数据上使用FL引入了联邦混合深度信念网络（FHDBN）模型。此外，IDFLM-ES技术使用基于数据归一化和金豺优化（GJO）的特征选择作为预处理步骤。此外，IDFLM-ES技术在分布式数据库上学习个体和分布式特征表示，以增强模型收敛以实现快速学习。最后，利用蜣螂优化器（DBO）模型选择FHDBN模型的有效超参数。使用基准数据库验证了IDFLM-ES方法的仿真值。IDFLM-ES方法的实验验证表明，与其他模型相比，其准确率高达98.24%。
