Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet
speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the …

Federated learning (FL) is a recent development in artificial intelligence, which is typically
based on the concept of decentralized data. As cyberattacks are frequently happening in the …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Secure model aggregation is a key component of federated learning (FL) that aims at
protecting the privacy of each user's individual model while allowing for their global …

The continuous emergence of new and sophisticated malware specifically targeting Android-
based Internet of Things devices is causing significant security hazards and is consequently …

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the
large volumes of data they produce and growing concerns of data privacy. However, there …

Federated Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

The growth of the internet over the years has resulted in massive use and spread of the
Internet of Things (IoT) in many areas. From home networks to industrial IoT, from medicine …