Empirical defenses for machine learning privacy forgo the provable guarantees of
differential privacy in the hope of achieving higher utility while resisting realistic adversaries …

Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various
domains, yet understanding the intricacies of DNN decision-making and learning processes …

Recently Diffusion-based Purification (DiffPure) has been recognized as an effective
defense method against adversarial examples. However, we find DiffPure which directly …

Decentralized Federated Learning (DFL) has garnered attention for its robustness and
scalability compared to Centralized Federated Learning (CFL). While DFL is commonly …

Quantifying the impact of individual data samples on machine learning models is an open
research problem. This is particularly relevant when complex and high-dimensional …

Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …

The raise of machine learning and deep learning led to significant improvement in several
domains. This change is supported by both the dramatic rise in computation power and the …

Adversarial robustness, the ability of a model to withstand manipulated inputs that cause
errors, is essential for ensuring the trustworthiness of machine learning models in real-world …

Distributed learning (DL) leverages multiple nodes to accelerate training, enabling the
efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key …

Backdoor attacks pose significant challenges to the security of machine learning models,
particularly for overparameterized models like deep neural networks. In this paper, we …