Abstract Internet of Things (IoT) is an emerging area in which billions of smart objects are
interconnected with each other using Internet for data and resource sharing. These smart …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while kee** privacy of training data on clients. Recently, many …

Decentralized trust management is used as a referral benchmark for assisting decision
making by human or intelligence machines in open collaborative systems. During any given …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …

Many computer vision tasks rely on feature extraction. Interest points are such features. This
paper shows that interest points are geometrically stable under different transformations and …

Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …

Certified_Watermarks is the first to provide a watermark certificate against $ l_2 $-norm
watermark removal attacks, by leveraging the randomized smoothing techniques for certified …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

Recent studies on federated learning (FL) have sought to solve the system heterogeneity
issue by designing customized local models for different clients. However, public dataset …