Although there are some protection mechanisms in federated learning, its training process is
still vulnerable to some powerful attacks, such as invisible backdoor attacks. Existing …

Federated learning (FL) has been recognized as a rapidly growing research area, where the
model is trained over massively distributed clients under the orchestration of a parameter …

The increased charging demand resulting from the rapid development of electric vehicles
(EVs) poses various challenges to the stable operation of the distribution network and smart …

Asynchronous evolutionary algorithms are becoming increasingly popular as a means of
making full use of many processors while solving computationally expensive search and …

Edge data analytics refers to processing near data sources at the edge of the network to
reduce delays in data transmission and, consequently, enable real-time interactions …

We have aspired as a green and intelligent future, where humans, the built environment,
and the nature are interconnected as a cyber-physical system. To such an Internet of Things …

Federated Learning (FL) allows the collaborative training of a global model in Vehicular Ad-
hoc Networks (VANETs): data is maintained on the owner's device and the local gradient …

Federated learning is a popular framework that enables harvesting edge resources'
computational power to train a machine learning model distributively. However, it is not …

Federated Learning is a promising technique for preserving data privacy that enables
communication between distributed nodes without the need for a central server. Previously …

The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its
aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in …