3 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.CR2025
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias Åkesson, Johan Östman +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…
cs.IT2021
On Joint Detection and Decoding in Short-Packet Communications
Alejandro Lancho, Johan Östman, Giuseppe Durisi
We consider a communication problem in which the receiver must first detect the presence of an information packet and, if detected, decode the message carried within it. We present…