In recent years, ransomware has evolved to target Internet of things (IoT) devices, such as medical equipment and thermostats. Traditional ransomware detection methods may not be effective for resource-constrained IoT devices as IoT-based ransomware is ...

This paper presents a new hardware prototype to explore how centralized and hierarchical federated learning systems are impacted by real-world devices distribution, availability, and heterogeneity. Our results show considerable learning performance ...

Due to the intermittent nature of solar energy, it has been increasingly challenging for the utilities, third-parties, and government agencies to integrate distributed energy resources generated by rooftop solar photovoltaic (PV) arrays into smart grids. ...

The inclusion of human-related aspects in the Internet of Things paradigm leads to the development of models and solutions that address several challenges of our society. The adoption of these novel approaches is expanding rapidly on the road to what is ...

The recent developments in Federated Learning (FL) focus on optimizing the learning process for data, hardware, and model heterogeneity. However, most approaches assume all devices are stationary, charging, and always connected to the Wi-Fi when ...

With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task ...

In this work, we present and evaluate a crowdsourcing platform to collect wearable IoT data with local differential privacy (LDP). LDP protects privacy by perturbing data with noise, which may hinder their utility in some cases. For this reason, most ...

In recent years, Internet of Things (IoT) devices have been extensively deployed in edge networks, including smart homes and offices. Despite the exciting opportunities afforded by the advancements in the IoT, it also introduces new attack vectors and ...

Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-...