3 papers
cs.CL2024
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…
cs.LG2024
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…
cs.LG2024
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…