13 citations · 23 across the 10 of their papers we have counts for
6 papers · 1 filter
RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks
Xingran Chen, Rohit Bhagat, Ghadir Ayache +3
Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large foundation models. Adopting fine-tuning to distributed settings faces severa…
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…
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…
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…