4 papers
Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
Wentao Yu, Sheng Wan, Shuo Chen +2
Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from di…
LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics
Jialiang Tang, Shuo Chen, Chen Gong +2
Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs),…
Hybrid Data-Free Knowledge Distillation
Jialiang Tang, Shuo Chen, Chen Gong
Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. E…
Modeling Inter-Intra Heterogeneity for Graph Federated Learning
Wentao Yu, Shuo Chen, Yongxin Tong +2
Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the h…