TFedSec-HI: Transformer-Driven Federated Security for IoT-Enabled Healthcare Industry 5.0 on Non-IID Data

The Internet of Things (IoT) enhances the healthcare industry 5.0 by enabling connected devices and data-driven treatments, but it also introduces cyber threats such as data breaches, and unauthorized access. Mobile Edge Computing (MEC) improves security by reducing reliance on cloud transmissions. However, challenges such as Non-Independent and Identically Distributed (Non-IID) data and device intermittency affect security models in healthcare that require real-time analytics and reliable automation. These limitations are crucial in sensitive medical applications that require real-time analytics and reliable automation. This paper proposes TFedSec-HI, a Transformerdriven Federated Learning (TFL) for improving threat detection in the healthcare industry 5.0. Network traffic data is converted to grayscale and multi-channel RGB images using Local Binary Patterns (LBP) and Sobel edge detection. A lightweight mobile Vision Transformer (ViT) is used for effective feature extraction on edge devices, reducing computational load while retaining high performance. The Federated Proximal (FedProx) algorithm is used during the model aggregation phase to address issues with non-IID data distribution, resulting in consistent and effective learning. The global model is then shared with clients for realtime threat classification. The proposed method is evaluated on two real-world datasets, CICIoT2023 and CICIoMT2024, resulting in exceptional classification accuracies of 99.18% and 99.74%, respectively. TFedSec-HI addresses the Non-IID data challenges in Industry 5.0 healthcare by utilizing TFL to enable private, and adaptive threat detection across medical IoT devices.