134 citations · 262 across the 9 of their papers we have counts for
9 papers
Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE
Holger R. Roth, Sarthak Tickoo, Mayank Kumar +18
Fraud-related financial losses continue to rise, while regulatory, privacy, and data-sovereignty constraints increasingly limit the feasibility of centralized fraud detection syste…
Optimizing Federated Learning in the Era of LLMs: Message Quantization and Streaming
Ziyue Xu, Zhihong Zhang, Holger R. Roth +3
Federated Learning (FL) offers a promising solution for training machine learning models across distributed data sources while preserving data privacy. However, FL faces critical c…
Secure Federated XGBoost with CUDA-accelerated Homomorphic Encryption via NVIDIA FLARE
Ziyue Xu, Yuan-Ting Hsieh, Zhihong Zhang +4
Federated learning (FL) enables collaborative model training across decentralized datasets. NVIDIA FLARE's Federated XGBoost extends the popular XGBoost algorithm to both vertical…
C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System
Parker Addison, Minh-Tuan H. Nguyen, Tomislav Medan +13
Organizations seeking to utilize Large Language Models (LLMs) for knowledge querying and analysis often encounter challenges in maintaining an LLM fine-tuned on targeted, up-to-dat…
Supercharging Federated Learning with Flower and NVIDIA FLARE
Holger R. Roth, Daniel J. Beutel, Yan Cheng +13
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicate…
Empowering Federated Learning for Massive Models with NVIDIA FLARE
Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh +12
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most stat…
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Holger R. Roth, Yan Cheng, Yuhong Wen +20
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVI…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu +8
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning al…