Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

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 …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …

Privacy concerns make it infeasible to construct a large medical image dataset by fusing
small ones from different sources/institutions. Therefore, federated learning (FL) becomes a …

Existing deep learning technologies generally learn the features of chest X-ray data
generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia …

Large-scale text-to-image generation models have achieved remarkable progress in
synthesizing high-quality, feature-rich images with high resolution guided by texts. However …

Privacy concerns often arise as the key bottleneck for the sharing of data between
consumers and data holders, particularly for sensitive data such as Electronic Health …

The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …