3 papers
cs.CR2025
Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy
Rouzbeh Behnia, Jeremiah Birrell, Arman Riasi +3
Federated learning (FL) enables organizations to collaboratively train models without sharing their datasets. Despite this advantage, recent studies show that both client updates a…
cs.CR2025
Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings
Nora Basha, Bechir Hamdaoui, Attila A. Yavuz +2
Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on th…
cs.CR2024
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
Rouzbeh Behnia, Arman Riasi, Reza Ebrahimi +3
Secure aggregation protocols ensure the privacy of users' data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant com…