Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …

To meet the grand challenges of agricultural production including climate change impacts
on crop production, a tight integration of social science, technology and agriculture experts …

With federated learning, one of the most notable features is that it can update global model
parameter without using the users' local data. However, various security and privacy …

Convolutional neural networks (CNNs) have been widely employed in image and video
recognition tasks due to their superior performance. With the increasing popularity of …

Traditionally, deep learning practitioners would bring data into a central repository for model
training and inference. Recent developments in distributed learning, such as federated …

Towards the realization of a super-smart society, AI analysis methods that preserve the
privacy of big data in cyberspace are being developed. From the viewpoint of developing …

Class incremental learning (CIL) aims to learn new classes from the data stream, where old
class data is largely discarded due to data privacy or memory restrictions. A handful of …

With the rapid development of cloud computing, outsourcing massive data and complex
deep learning model to cloud servers (CSs) has become a popular trend, which also brings …

Traditional recommendation systems require users to upload local data to the server to
generate recommendation results. In this process, users' privacy is easy to disclose …

Abstract Privacy-Preserving Deep Learning (PPDL) has been successfully applied in the
inference phase to preserve the privacy of input data. However, PPDL models are …