2 citations · 3 across the 20 of their papers we have counts for
6 papers · 1 filter
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Channel-Wise Mixed-Precision Quantization for Large Language Models
Zihan Chen, Bike Xie, Jundong Li +1
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substan…
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
Verification of Machine Unlearning is Fragile
Binchi Zhang, Zihan Chen, Cong Shen +1
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, foll…
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
Zihan Chen, Song Wang, Cong Shen +1
In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…