Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized
problem-solving technique that allows users to train using massive data. Unprocessed …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated learning (FL) is creating a paradigm shift in machine learning by directing the
focus of model training to where the data actually exist. Instead of drawing all data into a …

In recent years, significant progress has been made in the application of federated learning
(FL) in various aspects of cyberspace security, such as intrusion detection, privacy …

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 …

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of
individuals. This paper conducts a thorough study of the use of deep learning (DL) and …

Federated learning (FL) can tackle the problem of data silos of asymmetric information and
privacy leakage; however, it still has shortcomings, such as data heterogeneity, high …

Driver distraction detection (3D) is essential in improving the efficiency and safety of
transportation systems. Considering the requirements for user privacy and the phenomenon …

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-
scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal …