Objectives The objective was to review current research and practices for using FL to
generate synthetic data and determine the extent to which research has been undertaken …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization
due to data heterogeneity in an ever domain-shifting environment. While Federated …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

This paper proposes Zone-based Federated Learning (ZoneFL) to simultaneously achieve
good model accuracy while adapting to user mobility behavior, scaling well as the number of …

Federated learning (FL) enables resource-constrained node devices to learn a shared
model while keeping the training data local. Since recent research has demonstrated …

“Safety is our friend, accidents our enemy” is the propaganda social information on traffic
safety in several countries. This proves that safety is a top priority when using vehicles when …

Depression detection using deep learning models has been widely explored in previous
studies, especially due to the large amounts of data available from social media posts …

Federated Continual Learning (FCL) has emerged as a promising paradigm that combines
Federated Learning (FL) and Continual Learning (CL). To achieve good model accuracy …

Mobile apps, such as mHealth and wellness applications, can benefit from deep learning
(DL) models trained with mobile sensing data collected by smart phones or wearable …