The significance of distributed learning and inference algorithms in Internet of Things (IoT)
network is growing since they flexibly distribute computation load between IoT devices and …

The growing number of AI-driven applications in mobile devices has led to solutions that
integrate deep learning models with the available edge-cloud resources. Due to multiple …

More and more, edge devices embark Artificial Neuron Networks. In this context, a trend is to
simultaneously decentralize their training as much as possible while shrinking their resource …

Machine learning has found many applications in network contexts. These include solving
optimisation problems and managing network operations. Conversely, networks are …

Expanding the existing sensing systems to provide high-quality deep learning models for
more domains, such as new users or environments, is challenged by the limited labeled …

Large language models (LLMs) have been a disruptive innovation in recent years, and they
play a crucial role in our daily lives due to their ability to understand and generate human …

Federated Learning (FL) in edge Internet of Things (IoT) environments is challenging due to
the heterogeneous nature of the learning environment, mainly embodied in two aspects …

The use of Deep Neural Networks (DNNs) has skyrocketed in recent years. While its
applications have brought many benefits and use cases, they also have a significant …

Edge inference techniques partition and distribute Deep Neural Network (DNN) inference
tasks among multiple edge nodes for low latency inference, without considering the core …

Abstract Deep Neural Networks (DNNs) have gained widespread popularity in different
domain applications due to their dominant performance. Despite the prevalence of …