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@@ -89,6 +89,7 @@ This section partially refers to [DBLP](https://dblp.uni-trier.de/search?q=Feder
 | Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification | ZJU | IJCAI :mortar_board: | 2022 | VFGNN[^VFGNN] | [[PUB](https://www.ijcai.org/proceedings/2022/272).] [[PDF](https://arxiv.org/abs/2005.11903)] |
 | SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data | USC | AAAI:mortar_board: | 2022 | SpreadGNN[^SpreadGNN] | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20643).] [PDF](https://arxiv.org/abs/2106.02743) [[Code]](https://github.com/FedML-AI/SpreadGNN) [[解读](https://zhuanlan.zhihu.com/p/429720860)] |
 | FedGraph: Federated Graph Learning with Intelligent Sampling | UoA | TPDS :mortar_board:    | 2022 | FedGraph[^FedGraph] | [[PUB.]](https://ieeexplore.ieee.org/abstract/document/9606516/) [Code](https://github.com/cfh19980612/FedGraph) [[解读](https://zhuanlan.zhihu.com/p/442233479)] |
+| FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | CIKM Workshop (Oral) | 2022 | FedGCN[^FedGCN] | [[PDF](https://arxiv.org/abs/2201.12433)] [[Code](https://github.com/yh-yao/FedGCN)] |
 | FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction | UESTC | TMI | 2022 | FedNI[^FedNI] | [[PUB](https://ieeexplore.ieee.org/document/9815303).] [[PDF](https://arxiv.org/abs/2112.10166)] |
 | FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs | SYSU | TOIS | 2022 | FedEgo[^FedEgo] | [PUB.] [PDF](https://arxiv.org/abs/2208.13685) [Code](https://github.com/fedego/fedego) |
 | A federated graph neural network framework for privacy-preserving personalization | THU | Nature Communications | 2022 | FedPerGNN[^FedPerGNN] | [[PUB](https://www.nature.com/articles/s41467-022-30714-9).] [[Code](https://github.com/wuch15/FedPerGNN)] [[解读](https://zhuanlan.zhihu.com/p/487383715)] |
@@ -150,7 +151,6 @@ This section partially refers to [DBLP](https://dblp.uni-trier.de/search?q=Feder
 | Federated Graph Neural Networks: Overview, Techniques and Challenges **`surv.`** |                | preprint               | 2022 |                     | [[PDF](https://arxiv.org/abs/2202.07256)]                    |
 | Decentralized event-triggered federated learning with heterogeneous communication thresholds. | | preprint | 2022 | EF-HC[^EF-HC] | [PDF](https://github.com/ShahryarBQ/EF_HC) |
 | More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks |                | preprint               | 2022 |                     | [[PDF](https://arxiv.org/abs/2202.03195)]                    |
-| FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks |                | preprint               | 2022 | FedGCN[^FedGCN] | [[PDF](https://arxiv.org/abs/2201.12433)] [[Code](https://github.com/yh-yao/FedGCN)] |
 | Federated Learning with Heterogeneous Architectures using Graph HyperNetworks |                | preprint               | 2022 |                     | [[PDF](https://arxiv.org/abs/2201.08459)]                    |
 | STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks |                | preprint               | 2021 |  | [[PDF](https://arxiv.org/abs/2111.06750)] [[Code](https://github.com/jw9msjwjnpdrlfw/tsfl)] |
 | Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning |  | preprint | 2021 |  | [[PDF](https://arxiv.org/abs/2110.06468)] [[Code](https://github.com/hgh0545/graph-fraudster)] |