12 citations · 20 across the 6 of their papers we have counts for
7 papers
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
Yinong Wang, Jianwen Chen, Zhou Chen +28
We developed BrainVLM to classify all 12 World Health Organization (WHO) 2021 brain tumor types. BrainVLM integrates an uncertainty quantification strategy to indicate prediction r…
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
Weiying Zheng, Ziyue Lin, Pengxin Guo +3
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding and generation by integrating visual and textual information. While instruction…
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
Pengxin Guo, Runxi Wang, Shuang Zeng +7
Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that pri…
A New Federated Learning Framework Against Gradient Inversion Attacks
Pengxin Guo, Shuang Zeng, Wenhao Chen +4
Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demon…
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
Shuang Zeng, Pengxin Guo, Shuai Wang +3
Federated Learning (FL) is a rising approach towards collaborative and privacy-preserving machine learning where large-scale medical datasets remain localized to each client. Howev…
CD-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
Yiqing Shen, Yuyin Zhou, Lequan Yu
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains cha…
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang +5
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each inst…