Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Personalized decision-making can be implemented in a Federated learning (FL) framework
that can collaboratively train a decision model by extracting knowledge across intelligent …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

Abstract Federated-Learning (FL), a new paradigm in the machine-learning approach,
wherein the clients train the global model collaboratively across various computational …

The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare,
addressing critical challenges in securing electronic health records (EHRs), ensuring data …

Smart homes, powered mostly by Internet of Things (IoT) devices, have become very
popular nowadays due to their ability to provide a holistic approach towards effective energy …

Abstract The Internet of Health Things requires rigid security policies to control access to
sensitive data. However, nowadays, classic methods for user authentication may not meet …