Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

The digital transformation is characterized by the convergence of technologies — from the Internet of Things (IoT) to Edge-Fog-Cloud computing, Artificial Intelligence (AI), and Blockchain — in multiple dimensions blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus towards any reference models or best practices. This paper provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this paper by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

Advances in Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) are enabling remote health monitoring in everyday settings for early detection and prevention of chronic health problems. Such solutions can be used to augment a conventional physician-centered healthcare system. Stress, as one of the critical health problems, affects individuals adversely in terms of both physical and mental health. Prior studies on stress evaluation utilize a centralized cloud-based approach that combines data from each client for modeling. However, such a centralized approach raises data privacy concerns. To preserve privacy, decentralized federated learning has been proposed as a potential alternative framework. Nevertheless, existing federated learning algorithms have to deal with data heterogeneity; data skewness in each participant can significantly degrade the overall model performance. To tackle this challenge, we present a personalized, low-overhead clustered federated learning algorithm for stress-level recognition. The proposed algorithm outperforms two state-of-the-art baseline algorithms by providing over 7% and 12% increase in accuracy, respectively. The proposed algorithm also obtains a reduction of 37.5% and 9.6% in the training runtime compared to the two baseline algorithms. We also present a novel cold-start algorithm for new clients who join the trained system. Our results suggest that this cold-start algorithm is robust in terms of individual classification accuracy and total training time.