13 citations · 29 across the 7 of their papers we have counts for
7 papers
Backdoor Threats from Compromised Foundation Models to Federated Learning
Xi Li, Songhe Wang, Chen Wu +2
Federated learning (FL) represents a novel paradigm to machine learning, addressing critical issues related to data privacy and security, yet suffering from data insufficiency and…
Hierarchical Pretraining on Multimodal Electronic Health Records
Xiaochen Wang, Junyu Luo, Jiaqi Wang +5
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domai…
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Yuan Zhong, Suhan Cui, Jiaqi Wang +7
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in…
WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
Conghui He, Zhenjiang Jin, Chao Xu +6
The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs…
MLLM-DataEngine: An Iterative Refinement Approach for MLLM
Zhiyuan Zhao, Linke Ouyang, Bin Wang +5
Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes curre…
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Jiaqi Wang, Xingyi Yang, Suhan Cui +4
This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures.…