Deep learning techniques with neural networks are developing prominently in recent years
and have been deployed in numerous applications. Despite their great success, in many …

With the proliferation of machine learning, the cloud server has been employed to collect
massive data and train machine learning models. Several privacy-preserving machine …

Machine learning has become increasingly prominent and is widely used in various
applications in practice. Despite its great success, the integrity of machine learning …

Federated learning (FL), as a privacy-preserving machine learning framework, draws
growing attention in both industry and academia. It obtains a jointly accurate model by …

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

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

With the proliferation of cloud computing, data owners can outsource the spatial data from
the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage …

Neural network (NN) inference services enrich many applications, like image classification,
object recognition, facial verification, and more. These NN inference services are …

Machine unlearning aims to remove points from the training dataset of a machine learning
model after training; for example when a user requests their data to be deleted. While many …

It is becoming important for the client to be able to check whether the AI inference services
have been correctly calculated. Since the weight values in a CNN model are assets of …