7 citations · 9 across the 10 of their papers we have counts for
13 papers
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
Tongnian Wang, Carolina Vivas-Valencia, Cici Bauer +3
Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify pa…
Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies
Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source c…
Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening
Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging r…
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Yifan Gao, Yanmin Gong, Yun Shi +1
Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care. Wearable sensors such as…
FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models
Guohao Yang, Tongle Wu, Yuanxiong Guo +2
Fine-tuning is essential to adapt general-purpose large language models (LLMs) to domain-specific tasks. As a privacy-preserving framework to leverage decentralized data for collab…
FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices
Zhidong Gao, Yu Zhang, Zhenxiao Zhang +2
Despite demonstrating superior performance across a variety of linguistic tasks, pre-trained large language models (LMs) often require fine-tuning on specific datasets to effective…
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
Zhidong Gao, Zhenxiao Zhang, Yu Zhang +3
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been d…
One Node Per User: Node-Level Federated Learning for Graph Neural Networks
Zhidong Gao, Yuanxiong Guo, Yanmin Gong
Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a sol…
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
Zhidong Gao, Yu Zhang, Yanmin Gong +1
Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy…
DP-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
Zhenxiao Zhang, Yuanxiong Guo, Yanmin Gong
Federated learning (FL) is a distributed machine learning approach that allows multiple clients to collaboratively train a model without sharing their raw data. To prevent sensitiv…
Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks
Yu Zhang, Yanmin Gong, Lei Fan +3
Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper propo…
Efficient Entanglement Routing for Satellite-Aerial-Terrestrial Quantum Networks
Yu Zhang, Yanmin Gong, Lei Fan +3
In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet. This paper investigates the collaborati…
Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks
Yu Zhang, Yanmin Gong, Lei Fan +3
In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to p…