Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

In this work, the federated learning mechanism is introduced into the deep learning of
medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …

Over the last decade, smart cities (SC) have been developed worldwide. Implementing big
data and the internet of things improves the monitoring and integration of different …

With the rapid growth of mobile devices, wearable sensor-based human activity recognition
(HAR) has become one of the hottest topics in the Internet of Things. However, it is …

In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

As accurate and timely traffic flow information is extremely important for traffic management,
traffic flow prediction has become a vital component of intelligent transportation systems …

Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Federated learning (FL) enables multiple clients to jointly train a global learning model while
keeping their training data locally, thereby protecting clients' privacy. However, there still …

Abstract Machine learning models based on sensitive data in the real-world promise
advances in areas ranging from medical screening to disease outbreaks, agriculture …