Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

With the ever-increasing requirement of storage and computation resources, it is unrealistic
for local devices (with limited sources) to implement large-scale data processing. Therefore …

Federated learning (FL) is a machine learning technique that enables distributed devices to
train a learning model collaboratively without sharing their local data. FL-based systems can …

Attribute-based access control, which can provide fine-grained and flexible privacy
protection, is widely used in practice. Traditional attribute-based access control schemes …

As cloud storage has been widely adopted in various applications, how to protect data
privacy while allowing efficient data search and retrieval in a distributed environment …

By collecting and analyzing patients'e-healthcare data in Medical Internet-of-Things (MIOT),
e-Healthcare providers can offer alternative and helpful evaluation services of the risk of …

In this paper, we propose a lightweight privacy-preserving Faster R-CNN framework
(SecRCNN) for object detection in medical images. Faster R-CNN is one of the most …

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT)
and machine learning technologies have been widely used to achieve the intelligent control …

Natural language processing (NLP) allows a computer program to understand human
language as it is spoken, and has been increasingly deployed in a growing number of …