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. [Arxiv 2021] A Graph Federated Architecture with Privacy Preserving Learning. paper
4. [Arxiv 2021] Federated Myopic Community Detection with One-shot Communication. 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. [Arxiv 2021] FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. paper
7. [Arxiv 2021] FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search. paper
8. [Arxiv 2021] Cluster-driven Graph Federated Learning over Multiple Domains. paper
9. [Arxiv 2021] FedGL: Federated Graph Learning Framework with Global Self-Supervision. paper
10. [Arxiv 2021] Federated Graph Learning -- A Position Paper. paper
11. [Arxiv 2021] SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks. paper
12. [Arxiv 2021] Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. paper
13. [Arxiv 2021] A Vertical Federated Learning Framework for Graph Convolutional Network. paper
14. [Arxiv 2021] Federated Graph Classification over Non-IID Graphs. paper
15. [Arxiv 2021] Subgraph Federated Learning with Missing Neighbor Generation. paper

1. [Arxiv 2020] FedE: Embedding Knowledge Graphs in Federated Setting. paper
2. [Arxiv 2020] Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty. paper
3. [Arxiv 2021] Federated Knowledge Graphs Embedding.paper
4. [Arxiv 2021] Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries. paper

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

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