In this constantly evolving landscape of urbanization, the relationship between technology
and automation, in regards to sustainability, holds immense significance. The intricate …

Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced
privacy by eliminating data centralization and brings learning directly to the edge of the …

Federated learning is a decentralized learning approach that shows promise for preserving
users' privacy by avoiding local data sharing. However, the heterogeneous data in federated …

Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …

Distributed learning requires a frequent communication of neural network update data. For
this, we present a set of new compression tools, jointly called differential neural network …

A distributed machine learning technique called federated learning allows numerous
Internet of Things (IoT) edge devices to work together to train a model without sharing their …

Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for
collaboratively training machine learning models using distributed data with label deficiency …

Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living
standards, facilitating the rapid expansion of urban areas while efficiently managing …

The proliferation of IoT devices requires innovative approaches to gaining insights while
preserving privacy and resources amid unprecedented data generation. However, FL …

The late 1990s saw the rise of the edge computing network paradigm, as well as an
increase in the number of IoT de-vices. This concept is viewed as a link between cloud …