Federated Graph Neural Network

Federated Graph Neural Networks (FedGNNs) combine the power of graph neural networks (GNNs) for analyzing relational data with federated learning (FL) for privacy-preserving distributed training. Current research focuses on addressing challenges like data heterogeneity across clients, improving model accuracy and convergence speed, and mitigating vulnerabilities to backdoor and label inference attacks, often employing techniques like asynchronous computations and differential privacy within various GNN architectures. This field is significant for enabling collaborative machine learning on sensitive graph data in domains like healthcare and transportation, while also advancing our understanding of distributed learning and its security implications.

Papers

November 5, 2024

Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Jinyin Chen, Wenbo Mu, Luxin Zhang, Guohan Huang, Haibin Zheng, Yao Cheng
LeArning Abstract Adversarial Attack Federated Graph Neural Network

October 17, 2024

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna
Federated Learning Conformal Prediction Variational Method Subgraph Structure Thy Neighbor Graph Datasets Federated Graph Neural Network

October 15, 2024

Backdoor Attack on Vertical Federated Graph Neural Network Learning
Jirui Yang, Peng Chen, Zhihui Lu, Ruijun Deng, Qiang Duan, Jianping Zeng
Backdoor Attack Effective Backdoor Attack Federated Graph Neural Network

October 10, 2024

Federated Graph Learning for Cross-Domain Recommendation
Ziqi Yang, Zhaopeng Peng, Zihui Wang, Jianzhong Qi, Chaochao Chen, Weike Pan, Chenglu Wen, Cheng Wang, Xiaoliang Fan
Source Domain Cross Domain Recommendation Federated Graph Learning Domain Knowledge Transfer Domain Attention Federated Graph Neural Network

July 1, 2024

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
Wenhua Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
Multi Agent Reinforcement Learning Full Information Speech Based Age Intelligent Transportation System Intelligent Vehicle Federated Graph Neural Network Vehicular Edge Computing

June 27, 2024

Federated Graph Semantic and Structural Learning
Wenke Huang, Guancheng Wan, Mang Ye, Bo Du
Graph Structure Structure Learning Federated Graph Learning Federated Graph Neural Network Adjacency Hopping Graph Level Global Graph

January 5, 2024

Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network
Muhammad Yaqub, Shahzad Ahmad, Malik Abdul Manan, Imran Shabir Chuhan
Graph Neural Traffic Datasets Traffic Flow Spatial Temporal Graph Federated Graph Neural Network

November 24, 2023

Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks
R. Bourgerie, T. Zanouda
Anomaly Detection Federated Learning Fault Detection Federated Graph Neural Network Telecom Network

October 18, 2023

Equipping Federated Graph Neural Networks with Structure-aware Group Fairness
Nan Cui, Xiuling Wang, Wendy Hui Wang, Violet Chen, Yue Ning
Graph Neural Network Group Fairness Federated Graph Neural Network

September 18, 2023

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, Weiqiang Wang
Graph Neural Network Real Power Graph Level Task Federated Training Federated Graph Neural Network

August 4, 2023

Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas, Marina Krcek, Antonino Nocera, Stjepan Picek, Jing Xu
Zero Shot Vertical Federated Learning Label Inference Attack Federated Graph Neural Network

June 17, 2023

Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network
Fan Liu, Siqi Lai, Yansong Ning, Hao Liu
New Benchmark Backdoor Attack Federated Graph Neural Network Graph Backdoor Attack

September 13, 2022

Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets
Mehmet Yigit Balik, Arwa Rekik, Islem Rekik
Medical Datasets List Replicability Federated Graph Neural Network

June 7, 2022

A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy
Yeqing Qiu, Chenyu Huang, Jianzong Wang, Zhangcheng Huang, Jing Xiao
Graph Neural Network Differential Privacy Privacy Preserving Subgraph Structure Federated Graph Neural Network

February 15, 2022

Federated Graph Neural Networks: Overview, Techniques and Challenges
Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, Han Yu
Technical Challenge Gene Level GNN Barzilai Borwein Technique Graph Data Federated Graph Neural Network

February 7, 2022

More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Jing Xu, Rui Wang, Stefanos Koffas, Kaitai Liang, Stjepan Picek
Graph Neural Network Backdoor Attack Federated Graph Neural Network

January 28, 2022

FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong
Semi Supervised Node Classification Convergence Performance Federated Training Federated Graph Neural Network

Federated Graph Neural Network

Papers

Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Backdoor Attack on Vertical Federated Graph Neural Network Learning

Federated Graph Learning for Cross-Domain Recommendation

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

Federated Graph Semantic and Structural Learning

Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network

Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks

Equipping Federated Graph Neural Networks with Structure-aware Group Fairness

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks

Label Inference Attacks against Node-level Vertical Federated GNNs

Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network

Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets

A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy

Federated Graph Neural Networks: Overview, Techniques and Challenges

More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks

FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks