13 citations · 13 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
Zhenxiao Zhang, Zhidong Gao, Yuanxiong Guo +1
Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication lat…