federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

1. [Arxiv 2019] Peer-to-peer federated learning on graphs. paper
2. [NeurIPS Workshop 2019] Towards Federated Graph Learning for Collaborative Financial Crimes Detection. paper
3. [SPAWC 2021] A Graph Federated Architecture with Privacy Preserving Learning. paper
4. [Arxiv 2021] Federated Myopic Community Detection with One-shot Communication. paper
5. [ICCAD 2021] FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper. paper
6. [ICML 2023] Personalized Subgraph Federated Learning. paper

1. [Arxiv 2021] FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. paper
2. [Arxiv 2021] Federated Graph Learning -- A Position Paper. paper
3. [Arxiv 2022] Federated Graph Neural Networks: Overview, Techniques and Challenges paper

1. [Arxiv 2020] Federated Dynamic GNN with Secure Aggregation. paper
2. [Arxiv 2020] Privacy-Preserving Graph Neural Network for Node Classification. paper
3. [Arxiv 2020] ASFGNN: Automated Separated-Federated Graph Neural Network. paper
4. [Arxiv 2020] GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs. paper
5. [Arxiv 2021] FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation. paper
6. [ICLR-DPML 2021] FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. paper code
7. [Arxiv 2021] FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search. paper
8. [CVPR 2021] Cluster-driven Graph Federated Learning over Multiple Domains. paper
9. [Arxiv 2021] FedGL: Federated Graph Learning Framework with Global Self-Supervision. paper
10. [AAAI 2022] SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks. paper
11. [KDD 2021] Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. paper code
12. [Arxiv 2021] A Vertical Federated Learning Framework for Graph Convolutional Network. paper
13. [NeurIPS 2021] Federated Graph Classification over Non-IID Graphs. paper
14. [NeurIPS 2021] Subgraph Federated Learning with Missing Neighbor Generation. paper
15. [CIKM 2021] Differentially Private Federated Knowledge Graphs Embedding. paper code
16. [MICCAI Workshop 2021] A Federated Multigraph Integration Approach for Connectional Brain Template Learning. paper
17. [TPDS 2021] FedGraph: Federated Graph Learning with Intelligent Sampling. paper
18. [ACM TIST 2021] Federated Social Recommendation with Graph Neural Network paper
19. [CISS 2022] Decentralized Graph Federated Multitask Learning for Streaming Data paper
20. [JBHI 2022] Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications paper
21. [NeurIPS 2023] FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks paper code

1. [IJCKG 2021] FedE: Embedding Knowledge Graphs in Federated Setting. paper code
2. [Arxiv 2020] Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty. paper
3. [CIKM 2021] Federated Knowledge Graphs Embedding.paper
4. [Arxiv 2021] Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries. paper
5. [ACL Workshop 2022] Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation. paper code
6. [IJCAI 2022] Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. paper code

1. [IoTDI 2023] FedRule: Federated Rule Recommendation System with Graph Neural Networks paper code
2. [NeurIPS 2023 Dataset Track] Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking paper dataset

1. [IEEE Big Data 2019] A Graph Neural Network Based Federated Learning Approach by Hiding Structure. paper
2. [Arxiv 2020] Locally Private Graph Neural Networks. paper
3. [Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. paper
4. [Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. paper
5. [Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. paper

1. [IEEE Signal Processing Magazine 2019] Federated Learning：Challenges, Methods, and Future Directions. paper
2. [ACM TIST 2019] Federated Machine Learning Concept and Applications. paper
3. [IEEE Communications Surveys & Tutorials 2020] Federated Learning in Mobile Edge Networks A Comprehensive Survey. paper

1. [IEEE TNNLS 2020] A Comprehensive Survey on Graph Neural Networks. paper
2. [IEEE TKDE 2020] Deep Learning on Graphs: A Survey. paper
3. [AI Open] Graph Neural Networks: A Review of Methods and Applications. paper
4. [ArXiv 2021] Graph Neural Networks in Network Neuroscience. paper -- GitHub repo of all reviewed papers