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https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers

List: Awesome-Federated-Learning-on-Graph-and-GNN-papers

federated-learning gnn graph graph-neural-network knowledge-graph papers

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

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# Awesome-Federated-Learning-on-Graph-and-GNN-papers

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



## Federated Learning on Graphs

1. \[Arxiv 2019\] **Peer-to-peer federated learning on graphs.** [paper](https://arxiv.org/pdf/1901.11173)

2. \[NeurIPS Workshop 2019\] **Towards Federated Graph Learning for Collaborative Financial Crimes Detection.** [paper](https://arxiv.org/pdf/1909.12946)

3. \[SPAWC 2021\] **A Graph Federated Architecture with Privacy Preserving Learning.** [paper](https://arxiv.org/pdf/2104.13215)

4. \[Arxiv 2021\] **Federated Myopic Community Detection with One-shot Communication.** [paper](https://arxiv.org/pdf/2106.07255)

5. \[ICCAD 2021\] **FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper.** [paper](https://doi.org/10.1109/ICCAD51958.2021.9643440)

6. \[ICML 2023\] **Personalized Subgraph Federated Learning.** [paper](https://arxiv.org/abs/2206.10206)



## Federated Learning on Graph Neural Networks



### Survey Papers

1. \[Arxiv 2021\] **FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks.** [paper](https://arxiv.org/pdf/2104.07145) 

2. \[Arxiv 2021\] **Federated Graph Learning -- A Position Paper.** [paper](https://arxiv.org/pdf/2105.11099)

2. \[Arxiv 2022\] **Federated Graph Neural Networks: Overview, Techniques and Challenges** [paper](https://arxiv.org/pdf/2202.07256)



### Algorithm Papers



1. \[Arxiv 2020\] **Federated Dynamic GNN with Secure Aggregation.** [paper](https://arxiv.org/pdf/2009.07351)

2. \[Arxiv 2020\] **Privacy-Preserving Graph Neural Network for Node Classification.** [paper](https://arxiv.org/pdf/2005.11903)

3. \[Arxiv 2020\] **ASFGNN: Automated Separated-Federated Graph Neural Network.** [paper](https://arxiv.org/pdf/2011.03248)

4. \[Arxiv 2020\] **GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs.** [paper](https://arxiv.org/pdf/2012.04187)

5. \[Arxiv 2021\] **FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation.** [paper](https://arxiv.org/pdf/2102.04925)

6. \[ICLR-DPML 2021\] **FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks.** [paper](https://arxiv.org/pdf/2104.07145) [code](https://github.com/FedML-AI/FedGraphNN)

7. \[Arxiv 2021\] **FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search.** [paper](https://arxiv.org/pdf/2104.04141)

8. \[CVPR 2021\] **Cluster-driven Graph Federated Learning over Multiple Domains.** [paper](https://arxiv.org/pdf/2104.14628)

9. \[Arxiv 2021\] **FedGL: Federated Graph Learning Framework with Global Self-Supervision.** [paper](https://arxiv.org/pdf/2105.03170)

10. \[AAAI 2022\] **SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks.** [paper](https://arxiv.org/pdf/2106.02743)

12. \[KDD 2021\] **Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling.** [paper](https://arxiv.org/pdf/2106.05223) [code](https://github.com/mengcz13/KDD2021_CNFGNN)

13. \[Arxiv 2021\] **A Vertical Federated Learning Framework for Graph Convolutional Network.** [paper](https://arxiv.org/pdf/2106.11593)

14. \[NeurIPS 2021\] **Federated Graph Classification over Non-IID Graphs.** [paper](https://arxiv.org/pdf/2106.13423)

15. \[NeurIPS 2021\] **Subgraph Federated Learning with Missing Neighbor Generation.** [paper](https://arxiv.org/pdf/2106.13430)

16. \[CIKM 2021\] **Differentially Private Federated Knowledge Graphs Embedding.** [paper](https://arxiv.org/pdf/2105.07615) [code](https://github.com/HKUST-KnowComp/FKGE)

17. \[MICCAI Workshop 2021\] **A Federated Multigraph Integration Approach for Connectional Brain Template Learning.** [paper](https://link.springer.com/chapter/10.1007/978-3-030-89847-2_4)

18. \[TPDS 2021] **FedGraph: Federated Graph Learning with Intelligent Sampling.** [paper](https://ieeexplore.ieee.org/abstract/document/9606516/) 

19. [ACM TIST 2021] **Federated Social Recommendation with Graph Neural Network** [paper](https://arxiv.org/pdf/2111.10778)

20. [CISS 2022] **Decentralized Graph Federated Multitask Learning for Streaming Data** [paper](https://doi.org/10.1109/CISS53076.2022.9751160)

21. [JBHI 2022] **Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications** [paper](https://ieeexplore.ieee.org/document/9648036)

22. [NeurIPS 2023] **FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks** [paper](https://arxiv.org/abs/2201.12433) [code](https://github.com/yh-yao/FedGCN)



## Federated Learning on Knowledge Graph

1. \[IJCKG 2021\] **FedE: Embedding Knowledge Graphs in Federated Setting.** [paper](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) [code](https://github.com/AnselCmy/FedE)

2. \[Arxiv 2020\] **Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty.** [paper](https://arxiv.org/pdf/2011.11369)

3. \[CIKM 2021\] **Federated Knowledge Graphs Embedding.**[paper](https://arxiv.org/pdf/2105.07615)

4. \[Arxiv 2021\] **Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries.** [paper](https://arxiv.org/pdf/2107.05447)

5. \[ACL Workshop 2022\] **Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation.** [paper](https://arxiv.org/abs/2203.09553) [code](https://github.com/taokz/FedR)

6. \[IJCAI 2022\] **Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting.** [paper](https://arxiv.org/abs/2205.04692) [code](https://github.com/zjukg/MaKEr)



## Federated Graph Learning on IoT Devices

1. [IoTDI 2023] **FedRule: Federated Rule Recommendation System with Graph Neural Networks** [paper](https://arxiv.org/abs/2211.06812) [code](https://github.com/yh-yao/FedRule)

1. [NeurIPS 2023 Dataset Track] **Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking** [paper](https://openreview.net/forum?id=qynH28Y4xE) [dataset](https://huggingface.co/datasets/wyzelabs/RuleRecommendation)



## Private Graph Neural Networks

1. \[IEEE Big Data 2019\] **A Graph Neural Network Based Federated Learning Approach by Hiding Structure.** [paper](https://www.researchgate.net/profile/Shijun_Liu3/publication/339482514_SGNN_A_Graph_Neural_Network_Based_Federated_Learning_Approach_by_Hiding_Structure/links/5f48365d458515a88b790595/SGNN-A-Graph-Neural-Network-Based-Federated-Learning-Approach-by-Hiding-Structure.pdf)

2. \[Arxiv 2020\] **Locally Private Graph Neural Networks.** [paper](https://arxiv.org/pdf/2006.05535)

3. \[Arxiv 2021\] **Privacy-Preserving Graph Convolutional Networks for Text Classification.** [paper](https://arxiv.org/pdf/2102.09604)

4. \[Arxiv 2021\] **GraphMI: Extracting Private Graph Data from Graph Neural Networks.** [paper](https://arxiv.org/pdf/2106.02820)

5. \[Arxiv 2021\] **Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning.** [paper](https://arxiv.org/abs/2107.05917)



## Federated Learning: Survey

1. \[IEEE Signal Processing Magazine 2019\] **Federated Learning：Challenges, Methods, and Future Directions.** [paper](https://arxiv.org/pdf/1908.07873)

2. \[ACM TIST 2019\] **Federated Machine Learning Concept and Applications.** [paper](https://arxiv.org/pdf/1902.04885)

3. \[IEEE Communications Surveys & Tutorials 2020\] **Federated Learning in Mobile Edge Networks A Comprehensive Survey.** [paper](https://arxiv.org/pdf/1909.11875)



## Graph Neural Networks: Survey

1. \[IEEE TNNLS 2020\] **A Comprehensive Survey on Graph Neural Networks.** [paper](https://arxiv.org/pdf/1901.00596)

2. \[IEEE TKDE 2020\] **Deep Learning on Graphs: A Survey.** [paper](https://arxiv.org/pdf/1812.04202.pdf%E3%80%82)

3. \[AI Open\] **Graph Neural Networks: A Review of Methods and Applications.** [paper](https://www.sciencedirect.com/science/article/pii/S2666651021000012)

4. \[ArXiv 2021\] **Graph Neural Networks in Network Neuroscience.** [paper](https://arxiv.org/pdf/2106.03535.pdf) -- [GitHub repo of all reviewed papers](https://github.com/basiralab/GNNs-in-Network-Neuroscience)