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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
| Date | Stars |
|---|---|
| 2026-07-31 | 346 |
| 2026-08-05 | 346 |
| 2026-08-06 | 346 |
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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. [A
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:05fe591bbc8a671d, topic:knowledge-graph, desc:knowledge graph, readme:knowledge graph
matched fp:05fe591bbc8a671d, topic:papers