From the 1 of 3 linked papers with an AI index.
3 papers
Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera +1
The paper examines how efficiency‑focused optimizations for deploying large language models on edge devices create new security and privacy vulnerabilities, and it introduces a tax…
Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar +1
Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other client…
Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption
Muhammad Ali Najjar, Ren-Yi Huang, Dumindu Samaraweera +1
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine lear…