Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Systems for monitoring air quality are essential for reducing the negative consequences of
air pollution, but creating real-time systems encounters several challenges. The accuracy …

Federated learning (FL) has gained widespread popularity in recent years due to the fast
booming of advanced machine learning and artificial intelligence, along with emerging …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB).
Although the tuberculin skin test and interferon-gamma release assay can be used to …

Taxi-demand prediction is an important application of machine learning that enables taxi-
providing facilities to optimize their operations and city planners to improve transportation …

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing
how services and applications impact our daily lives. In traditional ML methods, data are …

Sufficient building operational data serve as the key premise to enable the development of
reliable data-driven technologies for building energy management. Considering that …

With the emergence of intelligent services and applications powered by artificial intelligence
(AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional …

Federated learning is currently a popular distributed machine learning solution that often
experiences cumbersome communication processes and challenging model convergence …