2 papers
cs.CR2026
Private and Robust Contribution Evaluation in Federated Learning
Delio Jaramillo Velez, Gergely Biczok, Alexandre Graell i Amat +2
Cross-silo federated learning allows multiple organizations to collaboratively train machine learning models without sharing raw data, but client updates can still leak sensitive i…
cs.CR2026
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias à kesson, Johan Ãstman +3
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…