34 citations · 47 across the 5 of their papers we have counts for
5 papers
Supercharging Federated Intelligence Retrieval
Dimitris Stripelis, Patrick Foley, Mohammad Naseri +4
RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built usi…
BadVFL: Backdoor Attacks in Vertical Federated Learning
Mohammad Naseri, Yufei Han, Emiliano De Cristofaro
Federated learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their data; rather, they train their own model locally and send u…
Cerberus: Exploring Federated Prediction of Security Events
Mohammad Naseri, Yufei Han, Enrico Mariconti +3
Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction mod…
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople +6
Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees. However, there is a discrepancy between the protection that su…
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, thi…