This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Federated learning (FL) is an emerging technology for empowering various applications that generate large amounts of data in intelligent cyber–physical systems (ICPS). Though FL can address users’ concerns about data privacy, its maintenance still depends on efficient incentive mechanisms. For long-term incentivization to participants in data federation under dynamic environments, deep reinforcement learning as a promising technology has been extensively studied. However, the nonstationary problem caused by the heterogeneity of ICPS devices results in a serious effect on the convergence rate of existing single-agent reinforcement learning. In this article, we propose a multiagent learning-based incentive mechanism to capture the stationarity approximation in FL with heterogeneous ICPS. First, we formulate the secure communication and data resource allocation problem as a Stackelberg game in FL with multiple participants. Then, to tackle the heterogeneous problem, we model this multiagent game as a partially observable Markov decision process. In particular, a multiagent federated reinforcement learning algorithm is proposed to learn the allocation policies efficiently by dwindling variances in policy evaluation caused by interaction among multiple devices without the requirement of sharing privacy information. Moreover, the proposed algorithm is proved to attain convergence at an expected rate. Finally, extensive experimental results demonstrate that our proposed algorithm significantly outperforms baseline approaches.

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA.

Intrusion detection systems (IDSs) integrated with machine learning (ML) techniques have proven to be effective defenses against the increasing cybersecurity attacks in the Artificial Intelligence of Things (AIoT) domain. Privacy concerns have prompted the emergence of federated learning (FL) as a promising solution for AIoT intrusion detection. Despite their potential, FL-based IDSs still face challenges related to class-imbalanced data and Non-Independent and Identically Distributed (non-IID) data among AIoT devices. These challenges hinder FL from learning meaningful features from the data, thus impeding the convergence of the learning process. To tackle these issues, this article proposes a clustering-enabled federated meta-training (CFMT) framework for AIoT intrusion detection. The proposed CFMT framework effectively addresses the negative impact of imbalanced and non-IID data. Specifically, we design a data- and model-agnostic meta-sampler that adaptively balances local data sets, thereby mitigating the data imbalance problem. Additionally, we propose a dynamic clustering algorithm that selectively eliminates the local models affected by the training state bias caused by non-IID data, thereby addressing the non-IID data issue. Extensive case studies on two real-world data sets demonstrate the superior performance of the proposed CFMT framework compared to existing solutions, including federated non-IID algorithms and federated imbalanced learning algorithms, in terms of IDS performance. Our code and data are available at https://gitee.com/mindspore/models/tree/master/research/cv/HDFL-IDS-Meta .

In the era of Industry 4.0, the Industrial Internet of Things (IIoT) has been applied to help physical entities to access real-time data in the network and share critical information. However, both the decentralization of IIoT and the heterogeneity of different devices constituting IIoT also pose a serious threat to secure communication between entities. Although the encryption technologies can ensure the confidentiality of communication data, the security of key becomes more important for devices in IIoT. Recently, some asymmetric group key agreement (AGKA) protocols have been proposed to negotiate a common encryption key for group members, and each group member holds its decryption key. However, the existing AGKA protocols fail to effectively control the access of agreement participants in IIoT. To solve this problem, in this article, we propose an AGKA protocol based on blockchain and attribute for IIoT. The proposed protocol not only realizes the access control of agreement participants but also realizes the automation of access control, the tamper resistance, and the nonrepudiation of the agreement process. The security and performance analysis of the proposed protocol show that it is more secure and effective compared with the existing AGKA protocols.