8 citations · 8 across the 12 of their papers we have counts for
13 papers
AgentFactory: Towards Automated Agentic System Design and Optimization
Enci Zhang, Haofeng Wang, Yuesheng Zhu +2
Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and complex task execution. Howev…
SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning
Yiyu Gui, Mingzhi Chen, Yuesheng Zhu +2
Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractive way to leverage unlabeled…
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…
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…