13 citations · 23 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
Zhenxiao Zhang, Zhidong Gao, Yuanxiong Guo +1
Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networ…