With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

Abstract Recent advances in Artificial Intelligence (AI) and the Internet of Things (IoT) have
facilitated continuous improvement in smart city based applications such as smart …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

Humanity is currently facing immense challenges related to the reduction of CO 2 emissions
and satisfying energy demand whilst mitigating environmental impacts, hence, developing …

Artificial intelligence (AI) plays a prominent role in smart cities' development and offers
benefits to different services such as finance, healthcare, security, agriculture, transport …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

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

Knowledge sharing and model personalization are two key components in the conceptual
framework of personalized federated learning (PFL). Existing PFL methods focus on …

Data mining is a process to extract unknown, hidden, and potentially useful information from
data. But the problem of data island makes it arduous for people to collect and analyze …