The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks is reshaping mobile edge computing (MEC) to support large-scale, heterogeneous Internet of Things (IoT) deployments and complex cyber-physical systems (CPS). Conventional data-driven intelligence in MEC traditionally relies on centralized learning paradigms that often fail to meet the privacy, latency, scalability, and adaptability requirements in distributed and resource-constrained environments. To address these shortcomings, the objective of our work in this paper is to investigate the paradigm shift toward distributed learning and demonstrate how its co-design with emerging communication and system-level technologies can enable scalable and trustworthy intelligence for next-generation IoT and CPS. Building on this objective, we conduct a systematic survey of recent studies and analyze twelve key enabling technologies, including concept drift adaptation, transformers, TinyML, blockchain, integrated sensing and communication (ISAC), digital twins, explainable AI, federated learning and unlearning, adversarial ML, meta-learning, and multi-armed bandits. The surveyed literature is organized using a unified taxonomy and an integrated conceptual pipeline, which clarifies how these enablers interact across sensing, communication, computation, trust, and adaptation layers of IoT and CPSs. The main outcomes of this study include: (i) a comprehensive taxonomy characterizing enabling technologies for distributed edge intelligence, (ii) a comparative synthesis of representative works highlighting common architectural patterns and evaluation practices, and (iii) the identification of research gaps, critical trade-offs, and open challenges, particularly related to model robustness, energy efficiency, data heterogeneity, and secure real-time inference. Overall, this survey establishes a structured foundation and forward-looking roadmap for designing scalable, privacy-preserving, and intelligent distributed learning systems in future B5G- and 6G-enabled IoT and CPS environments.

The integration of the Internet of Things (IoT) in healthcare has enabled intelligent and early heart attack detection (HAD) through artificial intelligence; however, the existing approaches suffer from high computational complexity and suboptimal performance, leading to inaccurate predictions, which can jeopardize patient well-being. Moreover, they fail to provide secure bidirectional communication between patients and medical centers while safeguarding patient privacy. Therefore, this paper addresses these limitations by proposing a novel secure and efficient HAD approach in healthcare. First, we propose a customized consortium blockchain network that leverages group signatures to ensure patient anonymity, data unlinkability, and secure two-way communication, thereby preserving patient privacy. Then, a lightweight, robust HAD model is devised via knowledge distillation by leveraging a novel proposed hybrid deep learning architecture that enables accurate early detection of heart attacks, supporting timely clinical intervention. Experimental results on a real dataset, i.e., the Cleveland dataset, demonstrate the scalability of the proposed approach that can process up to 500,000 patients in under 2.5 minutes, while preserving patient privacy. Moreover, it offers 99.22% accuracy, an F1-score of 99.23%, outperforming state-of-the-art techniques, with an inference time of 90 ms and a model memory footprint of only 0.28 MB.

Healthcare is revolutionized by the integration of the Internet of Medical Things (IoMT) and artificial intelligence (AI), enabling real-time patient monitoring, advanced predictive analytics, and personalized treatment plans. However, the existing AI healthcare models are typically trained offline on static datasets, limiting their adaptability to the dynamic nature of health data. This may result in compromising models' accuracy and healthcare decision-making, rendering them obsolete. Moreover, attackers may exploit concept drift by injecting frequent data shifts, which can exhaust healthcare institutions' resources. To address this research gap, we propose a novel adaptive, secure, and efficient concept drift detection framework for healthcare. First, a robust deep learning (DL) model is devised to leverage its high-confidence probability to detect data drift efficiently without relying on labeled data. Then, we propose a customized consortium blockchain network that leverages group signatures to ensure anonymity and unlinkability of patients' health data. It also utilizes a dualledger structure, facilitating a unified drift detection model and enabling authenticated, drift-specific data sharing among medical centers. This design protects against data tampering and falsely claiming drift incidents. Our experiments, conducted on a real health monitoring dataset, show that our concept drift detection approach achieves comparable drift detection performance to the existing methods while reducing the computational time by 52.35%, and achieving an accuracy of 98.43 with our offline model and a 95% accuracy with the online adaptive model.

Integrating the Internet of Medical Things (IoMT) and artificial intelligence (AI) is revolutionizing healthcare by enabling real-time health monitoring, predictive analytics, and personalized treatment. However, existing AI healthcare models are trained offline on static datasets, making them less adaptable to evolving health data and potentially reducing their accuracy and decision-making. Furthermore, adversaries may exploit this by injecting frequent data shifts, straining healthcare resources. Privacy concerns also arise from the exposure of sensitive patient data. Therefore, we propose a novel AI-driven healthcare methodology with secure, decentralized, and privacy-enhancing adaptive intelligence. First, a deep learning (DL) model is devised to leverage its high-confidence probability to detect data drift efficiently. Next, we propose a privacy-preserving approach leveraging functional encryption to ensure patient data confidentiality during drift detection and model retraining while eliminating reliance on a trusted entity. Lastly, we propose a customized consortium blockchain with group signatures to protect patient anonymity and data tampering and unlinkability while preventing falsely claiming drift incidents. Moreover, to ensure decentralization, it removes the need for a trusted authority in cryptographic key generation. Our experiments, on a real testbed and healthcare datasets, show that the proposed methodology achieves real-time drift detection with performance comparable to existing methods, while reducing the computational time by 52.35%. It also maintains high accuracy, achieving up to 98.43% with the offline health monitoring model and up to 96% with the online adaptive model. Additionally, it preserves patient privacy while reducing computational and communication overhead by 94.26% and 89%, respectively, compared to the state-of-the-art.

Heart attack detection (HAD) and human activity recognition (HAR) rely on wearable sensor data to track heart health and physical activity in real-time, facilitating early detection and monitoring of health issues. However, existing solutions for HAR and HAD face challenges in effectively capturing spatial features, long-term dependencies, and diverse sensor data representations. These shortcomings impact recognition accuracy, memory efficiency, and processing speed, while also demanding substantial computational resources due to their complexity. They also lead to performance degradation, increasing the risk of inaccurate diagnoses and potentially jeopardizing patient lives. To overcome these limitations, a novel lightweight hybrid architecture for HAR and HAD is proposed, leveraging convolutional neural networks (CNNs) with gated recurrent units (GRUs) and multi-head attention (MHA). CNNs capture spatial features, GRUs extract long-term dependencies, and MHA computes attention weights across data segments to highlight the most relevant features for health diagnosis, ensuring both improved performance and practicality for real-time health monitoring. Moreover, a magnitude-based weight pruning technique is adapted to reduce the proposed architecture’s complexity, making it suitable in resource-constrained settings without sacrificing accuracy. Furthermore, our methodology integrates an optimized genetic algorithm for counterfactual explanations, recommending minimal health data changes to lower heart attack risk. Experimental results on a real testbed and datasets, including PAMAP2, WISDM, and Cleveland, demonstrate that the proposed method outperforms the state-of-the-art methods, achieving up to 3% improvement in F1-score and accuracy, while reducing inference time, number of parameters, and memory footprint by over 40%, 70%, and 60%, respectively.

Load forecasting (LF) in smart grids is beneficial not only in mitigating equipment failures and power outages but also in facilitating effective power dispatching and infrastructure planning. To predict future loads accurately, the consumers' fine-grained energy consumption readings are fed into machine-learning (ML) models. However, revealing these readings enables adversaries to deduce confidential information about consumers, including details about their lifestyle, and hence their privacy is violated. To address this privacy issue, the existing works only focus on using federated learning (FL)-based approaches to train and obtain an accurate global LF model. Nevertheless, addressing the privacy violation problem during the LF process (in the deployment phase) after obtaining the global model for AMI networks has not been well investigated yet. Therefore, this paper proposes a novel, efficient, and decentralized approach that enhances the precision of LF while safeguarding the privacy of consumers. The proposed scheme incorporates inner product functional encryption (IPFE) to allow smart meters (SMs) to encrypt their readings with no need for a trusted key distribution center (KDC) while allowing LF without divulging or acquiring knowledge of the consumers' readings to protect their privacy. In addition, a hybrid deep learning approach is developed to construct an LF model that can yield precise forecasts. To show the feasibility of the proposed scheme, the performance of our scheme was assessed on a real energy consumption readings dataset, and the results demonstrate proficiency in LF while providing robustness and privacy preservation with reasonable communication efficiency.

Human activity recognition (HAR) leverages data from wearable devices and smartphones to detect actions, improving quality of life in areas like elderly care, health monitoring, and sports training. Current deep learning architectures struggle with extracting spatial features, long-term dependencies, and diverse sensor data representations, impacting recognition performance and posing challenges for resource-constrained IoT devices due to their complexity and parameter count. We propose a novel hybrid HAR architecture, integrating convolutional neural networks (CNN) and gated recurrent units (GRU) with a multi-head attention (MHA) mechanism. This architecture captures spatial features via CNN, extracts long-range dependencies with GRU, and uses MHA to compute attention weights for different data segments. The combined spatial and attention features are fed into a classification module for activity recognition. On the PAMAP2 dataset, our CNN-GRU-MHA model outperforms existing methods, achieving an F1-score of 98.4 %, with an inference time of 0.078 seconds and a memory footprint of 790.02 KB, reducing resource usage by 74.34 % and 62.81 %, respectively.