3 papers
cs.LG2025
WW-FL: Secure and Private Large-Scale Federated Learning
Felix Marx, Thomas Schneider, Ajith Suresh +3
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. However, recent…
cs.CR2025
Privacy-Preserving Epidemiological Modeling on Mobile Graphs
Daniel Günther, Marco Holz, Benjamin Judkewitz +4
The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, such as contact tracing, social distance regulations, and curf…
cs.CR2024
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Thomas Schneider, Ajith Suresh, Hossein Yalame
In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic e…