2 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
Baris Askin, Holger R. Roth, Zhenyu Sun +3
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by synchronization overhead. Asynch…
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…
VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge
Vishwesh Nath, Wenqi Li, Dong Yang +22
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is esse…