5 papers
Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT
Nikita Zeulin, Olga Galinina, Nageen Himayat +1
In the Industrial Internet of Things (IIoT) systems, edge devices often operate under strict constraints in memory, compute capability, and wireless bandwidth. These limitations ch…
Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
Nikita Zeulin, Olga Galinina, Ravikumar Balakrishnan +2
Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensiona…
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Haokun Chen, Sebastian Szyller, Weilin Xu +1
Large language models (LLMs) are trained using massive datasets, which often contain undesirable content such as harmful texts, personal information, and copyrighted material. To a…
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
Abhinav Kumar, George Torres, Noah Guzinski +6
The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless,…
Imperceptible Adversarial Examples in the Physical World
Weilin Xu, Sebastian Szyller, Cory Cornelius +5
Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing simil…