Recent studies have shown that, like traditional machine learning, federated learning (FL) is
also vulnerable to adversarial attacks. To improve the adversarial robustness of FL …

The increasing uncertainty of distributed energy resources promotes the risks of transient
events for power systems. To capture event dynamics, Phasor Measurement Unit (PMU) …

Abstract Deep Neural Networks are vulnerable to adversarial attacks. Among many defense
strategies, adversarial training with untargeted attacks is one of the most effective methods …

This paper seeks to address a gap in optimizing Average Precision (AP) while ensuring
adversarial robustness, an area that has not been extensively explored to the best of our …

Assigning importance weights to adversarial data has achieved great success in training
adversarially robust networks under limited model capacity. However, existing instance …

Adversarial examples are inputs to machine learning models that an attacker has
intentionally designed to confuse the model into making a mistake. Such examples pose a …

Abstract Extreme Multilabel Text Classification (XMTC) is a text classification problem in
which,(i) the output space is extremely large,(ii) each data point may have multiple positive …

Adversarial training has been proven to be one of the most effective techniques to defend
against adversarial examples. The majority of existing adversarial training methods assume …

We introduce a new learning framework, Fair Robust Active Learning (FRAL), generalizing
conventional active learning to fair and adversarial robust scenarios. This framework …

The functioning of flight test sensors is crucial for aviation safety, but previous methods often
overlooked the impact of data imbalance on model performance while exploring anomalies …