9 papers
DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models
Hanwen Zhang, Qiaojin Shen, Yuxi Liu +2
Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational deman…
Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
Shudong Liu, Hanwen Zhang, Xiuling Wang +2
One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communicat…
Inference Attacks Against Graph Generative Diffusion Models
Xiuling Wang, Xin Huang, Guibo Luo +1
Graph generative diffusion models have recently emerged as a powerful paradigm for generating complex graph structures, effectively capturing intricate dependencies and relationshi…
A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
Yufei Ma, Hanwen Zhang, Qiya Yang +2
In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmissio…
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
Zhekai Zhou, Shudong Liu, Zhaokun Zhou +4
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a shared model without sharing their local private data. Howeve…
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Jianhao Xie, Ziang Zhang, Zhenyu Weng +2
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solut…