Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Machine learning algorithms crucially depend on non-linear mathematical functions such as
division (for normalization), exponentiation (for softmax and sigmoid), tanh (as an activation …

Well-trained neural network models are deployed on edge servers to provide valuable
inference services for clients. To protect data privacy, a promising way is to exploit various …

Data privacy and security issues are preventing a lot of potential on-cloud machine learning
as services from happening. In the recent past, secure multi-party computation (MPC) has …

The growing volumes of data being collected and its analysis to provide better services are
creating worries about digital privacy. To address privacy concerns and give practical …

Graph convolutional network (GCN) is one of the most representative methods in the realm
of graph neural networks (GNNs). In the convolution process, GCN combines the structural …

Federated learning (FL) is a distributed machine learning paradigm in the Internet of Things
(IoT), which allows multiple devices to collaboratively train models without leaking local …

As convolutional neural networks (CNNs) exhibit remarkable performance in various
inference tasks, it is increasingly important to enable Internet of Things (IoT) devices to …

Graph convolutional networks (GCNs) are gaining popularity due to their powerful modelling
capabilities. However, guaranteeing privacy is an issue when evaluating on inputs that …

Double auction is one of the most promising solutions to allocate virtual machine (VM)
resources in two-sided cloud markets, which can increase the utilization rate of VM …