4 papers
Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning
Lukas Böhm, Arjhun Swaminathan, Anika Hannemann +1
Quantum Federated Learning (QFL) enables distributed training of Quantum Machine Learning (QML) models by sharing model gradients instead of raw data. However, these gradients can…
Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings
Arjhun Swaminathan, Mete Akgün
Deep neural networks for image classification remain vulnerable to adversarial examples -- small, imperceptible perturbations that induce misclassifications. In black-box settings,…
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal +1
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights…
PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal +2
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical pow…