1 citations · 2 across the 5 of their papers we have counts for
5 papers
FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity
Deepank Girish, Yi Hao Chan, Yubin Zheng +2
Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to general…
FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning
Yubin Zheng, Pak-Hei Yeung, Jing Xia +4
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without exposing local data, balancing performance and privacy. However, domain sh…
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Tianjie Ju, Yi Hua, Hao Fei +7
Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evide…
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency
Yubin Zheng, Peng Tang, Tianjie Ju +2
Medical image segmentation plays a vital role in clinic disease diagnosis and medical image analysis. However, labeling medical images for segmentation task is tough due to the ind…
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Tianjie Ju, Yijin Chen, Xinwei Yuan +4
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning. However, the reliability of LLMs in combining these two capa…