Federated learning (FL), a cutting-edge method of distributed learning, enables multiple
users to share training results while maintaining the privacy of their personal data. Collecting …

Solar irradiance forecasting is critical in the planning and operation of solar power plants for
production scheduling, energy trading, and maintenance planning. Accurate solar irradiance …

Nowadays, companies collect massive quantities of data to enhance their operations, often
at the expense of sharing user sensible information. This data is widely used to train Deep …

Differential privacy (DP) is a widely used technique for enhancing privacy in federated
learning (FL) frameworks, whereby noise is added to the datasets or learning parameters to …

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery
by connecting medical devices and applications to healthcare IT systems via the internet …

Internet of things (IoT) systems were becoming increasingly complex due to advancements
in open innovation, especially in the realms of intelligent automation and artificial …

As the applications of IoT continue to expand, the need for practical security measures
becomes increasingly vital. In previous studies, supervised machine learning models have …

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine
learning to succeed. However, the stringent privacy demands associated with mobile IoT …

This research is concerned with the critical domain of cybersecurity in edge computing
environments, which aims to strengthen defenses against increasing cyber threats that …

Individual and integrated uses of the federated learning (FL) and internet of things (IoT) have
lately been applied in numerous network-related situations, and as a result, they have been …