16 citations · 19 across the 8 of their papers we have counts for
5 papers · 1 filter
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
Zihan Liu, Yizhen Wang, Rui Wang +2
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. Wh…
Secure and Privacy-Preserving Vertical Federated Learning
Shan Jin, Sai Rahul Rachuri, Yizhen Wang +2
We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, fo…
Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
Shenao Yan, Shimaa Ahmed, Shan Jin +5
Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these models are vulnerable to backd…
SLVR: Securely Leveraging Client Validation for Robust Federated Learning
Jihye Choi, Sai Rahul Rachuri, Ke Wang +2
Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction at…
Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation
Sunpreet Arora, Andrew Beams, Panagiotis Chatzigiannis +9
One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data co…