The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

Industry 4.0 is having an increasingly positive impact on the value chain by modernizing and
optimizing the production and distribution processes. In this streamline, the digital twin (DT) …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Machine learning is one of the most prevailing techniques in computer science, and it has
been widely applied in image processing, natural language processing, pattern recognition …

Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …

Data poisoning attacks aim to manipulate the model produced by a learning algorithm by
adversarially modifying the training set. We consider differential privacy as a defensive …

Machine learning algorithms are vulnerable to data poisoning attacks. Prior taxonomies that
focus on specific scenarios, eg, indiscriminate or targeted, have enabled defenses for the …

Machine learning (ML) has been universally adopted for automated decisions in a variety of
fields, including recognition and classification applications, recommendation systems …

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by
slightly perturbing the features of correctly labeled training examples. By formalizing this …