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.CR2024
Privacy-Preserving Verifiable Neural Network Inference Service
Arman Riasi, Jorge Guajardo, Thang Hoang
Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simp…
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…