2 citations · 2 across the 4 of their papers we have counts for
4 papers
Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
Linh Tran, Ana Milanova, Stacy Patterson
Federated Learning (FL) with parameter-efficient fine-tuning, such as Low-Rank Adaptation (LoRA), enables scalable model training on distributed data. However, when combined with D…
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Linh Tran, Wei Sun, Stacy Patterson +1
Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated…
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
Linh Tran, Timothy Castiglia, Stacy Patterson +1
We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. P…
A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
Linh Tran, Sanjay Chari, Md. Saikat Islam Khan +3
We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applicati…