2 papers
cs.CR2025
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,…
cs.CR2024
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud
Marcin Chrapek, Anjo Vahldiek-Oberwagner, Marcin Spoczynski +3
Foundation Models (FMs) display exceptional performance in tasks such as natural language processing and are being applied across a growing range of disciplines. Although typically…