7 papers
FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning
Holger R. Roth, Ziyue Xu, Peter Cnudde
Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-d…
Auto-FL-Research: Agentic Search for Federated Learning Algorithms
Holger R. Roth, Ziyue Xu, Chester Chen +3
Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local training schedules, normaliz…
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…
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…
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…