Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process
the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT …

Deep learning, especially deep neural networks (DNNs), has been widely and successfully
adopted in many critical applications for its high effectiveness and efficiency. The rapid …

Semi-supervised learning (SSL) is gaining popularity as it reduces cost of machine learning
(ML) by training high performance models using unlabeled data. In this paper, we reveal that …

Federated learning is a popular framework designed to perform the distributed machine
learning while protecting client privacy. However, the heterogeneous data distribution in real …

3D mesh classification deep neural network (3D DNN) has been widely applied in many
safety-critical domains. Backdoor attack is a serious threat that occurs during the training …

Deep neural networks (DNNs) are increasingly used as the critical component of
applications, bringing high computational costs. Many practitioners host their models on …

Backdoor attacks have emerged as an urgent threat to Deep Neural Networks (DNNs),
where victim DNNs are furtively implanted with malicious neurons that could be triggered by …

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world
scenarios, but this is often overlooked by studies on FL security and privacy. On the one …

Blockchain-based federated learning, also being named as swarm learning, is perceived to
have great potential to support decentralized and privacy-enhancing big data processing …