With the recent emergence of artificial intelligence (AI), edge users in industries and manufacturing have been extensively using AI-based services, which pose privacy and security risks to data. Distributed learning approaches deployed in the manufacturing industries help reduce data risks. However, traditional approaches cannot handle deep models as well as the scalability of edge-based internet of things (E-IoT) devices, especially in the manufacturing sector. Studies have proposed SplitFed learning (SFL) by combining split learning and the federated learning paradigm, but they fail to achieve an optimal trade-off between communication and computational limitations and are vulnerable to inversion attacks. We present a pruning-oriented security (POST) method that is designed around the SFL paradigm that not only helps in achieving a balance between communication and computation load for E-IoT devices, but also preserves the data and model privacy against inversion attacks. The POST leverages the concept of using a higher number of layers in the E-IoT devices, which restrains the attacker from reconstructing the outputs. Furthermore, the POST adds communication and computation constraints in the optimization function to reduce the overall cost of the method. The novel pruning method adopts the regularization and adversarial training approach to further improve the preservation of the privacy of intermediate features in the SFL paradigm. We conduct our experiments on publicly available datasets in real-world settings to illustrate the efficacy of the POST method in terms of preserving privacy while ensuring the best trade-off for communication and computation load.

Sixth Generation (6G) wireless networks with ultra-low latency, high reliability, and massive connectivity require intelligent and privacy-concerned infrastructure optimization. This work presents a federated autoencoder platform combined with Explainable AI (XAI) for performance optimization of 6G-IoT systems. The method integrates traditional machine learning algorithms (Decision Tree, Random Forest, Logistic Regression, AdaBoost, Gradient Boosting) with a Variational Autoencoder (VAE) for dimensionality reduction and feature extraction. Federated Learning (FL) is utilized to maintain data privacy among distributed edge nodes, and SHAP and LIME explainers are utilized for explaining model decisions at the local and global levels. The framework points out key QoS parameters like latency and throughput as major optimization levers. Experimental outcomes on the 6G-IoT dataset indicate that Random Forest with highest accuracy for 80:20 split and Gradient Boosting has a 99.8% accuracy in a 10-fold validation, and FL gets a ROC-AUC value of 0.999 with robust privacy guarantees. XAI enhances transparency and regulatory compliance by making attribution of predictions to contributing features. As a whole, the proposed approach provides an interpretable, privacy-conscientious, and scalable tool for intelligent 6G-IoT infrastructure management.

The emergence of Internet of Things (IoT) devices in healthcare environments presents unprecedented opportunities for remote patient monitoring, telemedicine, and intelligent medical data processing. Current network management approaches fail to accommodate the unique characteristics of medical environments, where emergency cardiac monitoring systems require guaranteed millisecond response times while routine administrative systems can tolerate moderate delays. Existing solutions lack the domain knowledge to automatically distinguish between life-critical and standard medical traffic, resulting in inefficient resource allocation and potential patient safety risks. Therefore, this paper proposes a novel intent-based networking framework integrating knowledge-driven policy generation with multi-path network slicing for healthcare IoT ecosystems. Our approach introduces a multi-stage intent translation mechanism that leverages external medical knowledge graphs and deep learning techniques to decompose high-level healthcare service intents into executable network configurations automatically. Results show that the proposed framework achieves 85-98% resource utilization efficiency across varying network conditions, maintains 99.5% quality of service achievement for emergency response systems, and exhibits superior scalability with sub-150 millisecond response times when supporting 1000 IoT devices, enabling hospitals to reduce network configuration complexity by 78% while maintaining critical system uptime.

Deploying large-scale foundation models (FMs) in resource-constrained devices presents critical challenges due to their substantial computational and memory requirements. This is particularly relevant for multi-task wireless sensing FMs running on sensors. To overcome these limitations, we propose a tiny federated wireless foundation model (WFM) framework that combines spectrogram-guided structured block-wise pruning with federated learning (FL) for efficient on-device deployment. Our approach prunes non-essential encoder blocks in vision transformers (ViTs) by leveraging the masked spectrogram modeling (MSM) pretraining loss as an importance indicator, ensuring only the most structurally significant components are retained. This enables federated adaptation with frozen backbones and lightweight, task-specific heads, minimizing both computational burden and communication overhead. The pruning strategy preserves the integrity of spectrogram reconstruction, while federated fine-tuning supports decentralized learning across clients with heterogeneous data distributions. Experimental results on human activity sensing and radio signal identification tasks confirm the efficacy of our approach. Specifically, the pruned ViT-based WFMs achieve up to 93% multiply-accumulate operations (MACs) reduction, 85% lower CPU inference time, and 49% reduction in communication overhead, all while maintaining high task accuracy. Our method demonstrates strong generalization and robustness across varying pruning ratios and data heterogeneity levels, while substantially reducing communication overhead, making it highly suitable for real-world industrial IoT deployments.

The optimization of modern healthcare facilities presents unique challenges at the intersection of medical service quality, energy efficiency, and environmental impact. By integrating carbon-intelligent Industrial Internet of Things (IIoT) technologies with healthcare operations, our approach enables real-time monitoring and optimization of carbon emissions while maintaining medical service quality. Specifically, this paper presents a novel deep reinforcement learning-based multi-objective optimization algorithm (HC-DMOPSO) for IIoT-enabled healthcare building management. By integrating healthcare-specific constraints with an enhanced swarm intelligence framework, our approach optimizes building operations while considering medical device power demands, patient comfort, and environmental requirements. The proposed algorithm combines dual-distance metrics -population average distance and crowding distance -with deep Q-networks to effectively explore the complex solution space. Experimental results demonstrate HC-DMOPSO’s superior performance across multiple metrics: The integration of carbon intelligent IIoT sensors and actuators enables HC-DMOPSO to achieve 24.8% reduction in energy consumption while maintaining 99.92% medical power reliability, 32.5% decrease in peak load with only 0.38∘C average temperature deviation, and 28.7% improvement in carbon reduction compared to baseline methods.