# Awesome Collaborative GNNs [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

> Official repository for the survey paper: **"From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning"**

This repository provides a comprehensive collection of research papers on collaborative learning with Graph Neural Networks (GNNs), organized by objectives and graph partitioning strategies.

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## Overview

![Collaborative Learning Illustration](assets/tabular_to_graph.png)

**Collaborative Learning from Euclidean to Graph Data.** Collaborative learning refers to a system of agents that collectively train and infer ML models on their local private data. Collaborative learning has been extensively studied for Euclidean data, comprising highly popular domains like vision, language, temporal sequences, and measurement data. Conversely, collaborative learning on graph data is an emerging field. Graphs canonically represent data structured with relations, arising in domains including human behavior, physical systems, chemistry, and information systems. Compared to *centralized learning*, collaborative learning offers three main advantages: enabling training and inference on distributed datasets, communication efficiency, and privacy preservation of agents data. Collaborative learning on graph data aligns naturally with *Message Passing Neural Networks (MPNNs)*.

### Representative Works 🌟

Papers marked with a star (🌟) are representative works in their respective categories.

### Abstract of the survey

The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability. To address these challenges, recent research has explored *collaborative learning* approaches, including *federated learning* and *decentralized learning*, where individual agents perform training and inference locally, with limited collaboration.
Most collaborative learning research focuses on Euclidean data with regular, grid-like structure (e.g., images, text). However, these approaches fail to capture the relational patterns in many real-world applications, best represented by graphs. Learning on graphs relies on message-passing mechanisms to propagate information between connected nodes, making it conceptually well-suited for collaborative environments where agents must exchange information. Yet, the opportunities and challenges of learning on graph-structured data in collaborative settings remain largely underexplored.
This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data, aiming to consolidate this emerging field. We begin by reviewing its foundational principles for Euclidean data, organizing them along three core dimensions: learning effectiveness, efficiency, and privacy preservation. We then extend the discussion to graph-structured data, introducing a taxonomy of graph distribution scenarios, characterizing associated statistical heterogeneities, and developing standardized problem formulations and algorithmic frameworks. Finally, we systematically identify open challenges and promising research directions.
By bridging established techniques for Euclidean data with emerging methods for graph learning, our survey provides researchers and practitioners with a well-structured foundation of collaborative learning, supporting further development across a wide range of scientific and industrial fields


## Table of Contents

- [Organized by Trilemma Objectives](#organized-by-learning-objectives)
  - [Improving Learning and Inference Effectiveness](#improving-learning-and-inference-effectiveness)
  - [Improving Communication and Computation Efficiency](#improving-communication-and-computation-efficiency)
  - [Privacy-Preserving Techniques](#privacy-preserving-techniques)
- [Organized by Graph Partitioning](#organized-by-graph-partitioning)
  - [Subgraph - Homogeneous](#subgraph---homogeneous)
  - [Subgraph - Heterogeneous](#subgraph---heterogeneous)
  - [Multiple Graph Instances](#multiple-graph-instances)

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## Organized by Trilemma Objectives

![Trilemma](assets/trilemna.png)

**The Trilemma of Collaborative Learning.** Collaborative learning balances three competing objectives: **effectiveness**, **efficiency**, and **privacy**.

### Improving Learning and Inference Effectiveness

#### Data-based Techniques

**Data Augmentation**
- **[Subgraph Federated Learning with Missing Neighbor Generation](https://proceedings.neurips.cc/paper/2021/hash/34adeb8e3242824038aa65460a47c29e-Abstract.html)** - Zhang et al., NeurIPS 2021 🌟
- [Fedni: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://ieeexplore.ieee.org/document/9815303/) - Peng et al., TMI 2022
- [Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs](https://ieeexplore.ieee.org/document/10305592/) - Pei et al., TIFS 2023
- [Towards Private Learning on Decentralized Graphs with Local Differential Privacy](https://ieeexplore.ieee.org/document/9855440/) - Lin et al., TIFS 2022
- [Fully-Decentralized Training of GNNs Using Layer-Wise Self-Supervision](https://arxiv.org/abs/2302.08691) - Giaretta & Girdzijauskas, 2023
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- **[No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950)** - Agrawal et al., AAAI 2024 🌟
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14) - Qiu et al., KSEM 2022
- **[FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://www.sciencedirect.com/science/article/abs/pii/S002002552301561X)** - Chen et al., Information Sciences 2024 🌟
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024
- [An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning](https://link.springer.com/chapter/10.1007/978-3-031-30637-2_50) - Guo et al., DASFAA 2023
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023
- [BI-FedGNN: Federated Graph Neural Networks Framework Based on Bayesian Inference](https://www.sciencedirect.com/science/article/abs/pii/S0893608023005786) - Gao et al., Neural Networks 2024
- [FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference](https://proceedings.neurips.cc/paper_files/paper/2024/hash/3d226fb8fbd6ee6ec70d0427f1319707-Abstract-Conference.html) - Tan et al., NeurIPS 2024

**Local Regularization**
- **[Federated Graph Learning with Graphless Clients](https://openreview.net/forum?id=mVAp0eDfyR)** - Fu et al., TMLR 2024 🌟
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022
- [Personalized Subgraph Federated Learning](https://proceedings.mlr.press/v202/baek23a.html) - Baek et al., ICML 2023
- **[Subgraph Federated Learning for Local Generalization](https://arxiv.org/abs/2503.03995)** - Kim et al., ICLR 2025 🌟
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024
- [FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling](https://arxiv.org/abs/2501.12592) - Ceyani et al., SDM 2025
- [S2FGL: Spatial Spectral Federated Graph Learning](https://arxiv.org/abs/2507.02409) - Tan et al., ICML 2025
- **[FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference](https://proceedings.neurips.cc/paper_files/paper/2024/hash/3d226fb8fbd6ee6ec70d0427f1319707-Abstract-Conference.html)** - Tan et al., NeurIPS 2024 🌟

**Subsampling**
- [Fedgraph: Federated Graph Learning with Intelligent Sampling](https://ieeexplore.ieee.org/document/9606516) - Chen et al., TPDS 2021
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- [Federated Graph Learning with Periodic Neighbour Sampling](https://doi.org/10.1109/IWQoS54832.2022.9812908) - Du et al., IWQoS 2022
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021
- [FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling](https://arxiv.org/abs/2501.12592) - Ceyani et al., SDM 2025
- **[FedSPA: Generalizable Federated Graph Learning Under Homophily Heterogeneity](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)** - Tan et al., CVPR 2025 🌟
- **[EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification](https://openreview.net/forum?id=Bd9JlrqZhN)** - Shi et al., ICML 2025 🌟

#### Model-based Techniques

**Personalization of Embeddings or Parameters**
- [Fedsheafhn: Personalized Federated Learning on Graph-Structured Data](https://doi.org/10.48550/arXiv.2405.16056) - Liang et al., 2024
- [Subgraph-Level Federated Graph Neural Network for Privacy-Preserving Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0893608024004982) - Han et al., Neural Networks 2024
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [GFedKG: GNN-based Federated Embedding Model for Knowledge Graph Completion](https://www.sciencedirect.com/science/article/abs/pii/S0950705124009249) - Wang et al., KBS 2024
- [Semi-Decentralized Federated Ego Graph Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3543507.3583337) - Qu et al., WWW 2023
- [Graphfl: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://ieeexplore.ieee.org/document/10027604/) - Wang et al., ICDM 2022
- **[FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017)** - Zhang et al., TKDD 2023 🌟
- **[Personalized Subgraph Federated Learning](https://proceedings.mlr.press/v202/baek23a.html)** - Baek et al., ICML 2023 🌟
- [Federated Graph Classification over Non-Iid Graphs](https://proceedings.neurips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html) - Xie et al., NeurIPS 2021
- [Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning](https://doi.org/10.1609/aaai.v39i16.33814) - Fang et al., AAAI 2025
- **[EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification](https://openreview.net/forum?id=Bd9JlrqZhN)** - Shi et al., ICML 2025 🌟

**Architecture Personalization**
- [Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling](https://doi.org/10.1145/3447548.3467371) - Meng et al., KDD 2021
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://doi.org/10.1109/ACCESS.2022.3159688) - Krasanakis et al., IEEE Access 2022
- [Federated Graph Learning with Graphless Clients](https://openreview.net/forum?id=mVAp0eDfyR) - Fu et al., TMLR 2024
- [Federated Graph Neural Network for Cross-Graph Node Classification](https://doi.org/10.1109/CCIS53392.2021.9754598) - Guan et al., CCIS 2021
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023
- [Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning](https://doi.org/10.1609/aaai.v39i16.33814) - Fang et al., AAAI 2025
- **[EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification](https://openreview.net/forum?id=Bd9JlrqZhN)** - Shi et al., ICML 2025 🌟
- **[Spreadgnn: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data](https://doi.org/10.1609/aaai.v36i6.20643)** - He et al., AAAI 2022 🌟

#### Training and Inference Under Vertical Partitions

**Aggregating Node Representations**
- [Federated Learning over Coupled Graphs](https://doi.org/10.1109/TPDS.2023.3240527) - Lei et al., TPDS 2023
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://dl.acm.org/doi/10.1145/3583780.3614834) - Zheng et al., CIKM 2023
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://doi.org/10.48550/arXiv.2106.11593) - Ni et al., 2021
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022
- [Subgraph-Level Federated Graph Neural Network for Privacy-Preserving Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0893608024004982) - Han et al., Neural Networks 2024
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- [Efficient Federated Learning on Knowledge Graphs via Privacy-Preserving Relation Embedding Aggregation](https://aclanthology.org/2022.findings-emnlp.43) - Zhang et al., ACL 2022
- **[A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9)** - Wu et al., Nature Communications 2022 🌟
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14) - Qiu et al., KSEM 2022
- [Semi-Decentralized Federated Ego Graph Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3543507.3583337) - Qu et al., WWW 2023
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [GFedKG: GNN-based Federated Embedding Model for Knowledge Graph Completion](https://www.sciencedirect.com/science/article/abs/pii/S0950705124009249) - Wang et al., KBS 2024
- **[Fedsgc: Federated Simple Graph Convolution for Node Classification](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf)** - Cheung et al., FTL-IJCAI 2021 🌟

**Aggregating Graph Representations**
- **[Semi-Supervised Graph Instance Transformer for Mental Health Inference](https://doi.org/10.1109/ICMLA52953.2021.00198)** - Dong et al., ICMLA 2021 🌟

**Transfer Techniques**
- [Federated Graph Neural Network for Cross-Graph Node Classification](https://doi.org/10.1109/CCIS53392.2021.9754598) - Guan et al., CCIS 2021
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024

---

### Improving Communication and Computation Efficiency

#### Aggregation Patterns

**Centralized (Models)**
- [FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](http://papers.nips.cc/paper_files/paper/2023/hash/fc07feae9af49dd3f1a1e049b77f4e17-Abstract-Conference.html) - Yao et al., NeurIPS 2023
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://doi.org/10.24963/ijcai.2022/272) - Chen et al., IJCAI 2021
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14) - Qiu et al., KSEM 2022
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://www.sciencedirect.com/science/article/abs/pii/S002002552301561X) - Chen et al., Information Sciences 2024
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- [A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9) - Wu et al., Nature Communications 2022
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693) - Yan et al., WWW 2024
- [GFedKG: GNN-based Federated Embedding Model for Knowledge Graph Completion](https://www.sciencedirect.com/science/article/abs/pii/S0950705124009249) - Wang et al., KBS 2024
- [Semi-Decentralized Federated Ego Graph Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3543507.3583337) - Qu et al., WWW 2023
- [Subgraph-Level Federated Graph Neural Network for Privacy-Preserving Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0893608024004982) - Han et al., Neural Networks 2024
- [Efficient Federated Learning on Knowledge Graphs via Privacy-Preserving Relation Embedding Aggregation](https://aclanthology.org/2022.findings-emnlp.43) - Zhang et al., ACL 2022
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021
- [Graphfl: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://ieeexplore.ieee.org/document/10027604/) - Wang et al., ICDM 2022
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023
- **[Personalized Subgraph Federated Learning](https://proceedings.mlr.press/v202/baek23a.html)** - Baek et al., ICML 2023 🌟
- **[Spreadgnn: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data](https://doi.org/10.1609/aaai.v36i6.20643)** - He et al., AAAI 2022 🌟
- [Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning](https://doi.org/10.1609/aaai.v39i16.33814) - Fang et al., AAAI 2025
- **[EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification](https://openreview.net/forum?id=Bd9JlrqZhN)** - Shi et al., ICML 2025 🌟
- [Federated Graph Classification over Non-Iid Graphs](https://proceedings.neurips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html) - Xie et al., NeurIPS 2021
- [An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning](https://link.springer.com/chapter/10.1007/978-3-031-30637-2_50) - Guo et al., DASFAA 2023
- [BI-FedGNN: Federated Graph Neural Networks Framework Based on Bayesian Inference](https://www.sciencedirect.com/science/article/abs/pii/S0893608023005786) - Gao et al., Neural Networks 2024
- [FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling](https://arxiv.org/abs/2501.12592) - Ceyani et al., SDM 2025
- **[FedSPA: Generalizable Federated Graph Learning Under Homophily Heterogeneity](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)** - Tan et al., CVPR 2025 🌟
- **[Fedsgc: Federated Simple Graph Convolution for Node Classification](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf)** - Cheung et al., FTL-IJCAI 2021 🌟

**Centralized (Embeddings/Features)**
- **[FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](http://papers.nips.cc/paper_files/paper/2023/hash/fc07feae9af49dd3f1a1e049b77f4e17-Abstract-Conference.html)** - Yao et al., NeurIPS 2023 🌟
- [Federated Graph Learning with Periodic Neighbour Sampling](https://doi.org/10.1109/IWQoS54832.2022.9812908) - Du et al., IWQoS 2022
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://doi.org/10.24963/ijcai.2022/272) - Chen et al., IJCAI 2021
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://doi.org/10.48550/arXiv.2106.11593) - Ni et al., 2021
- [Adversarial Attacks on GNN-Based Vertical Federated Learning](https://doi.org/10.1016/j.cose.2023.103601) - Chen et al., Springer 2024
- **[Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233)** - Chen et al., IJCKG 2021 🌟
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- [A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9) - Wu et al., Nature Communications 2022
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://www.sciencedirect.com/science/article/abs/pii/S002002552301561X) - Chen et al., Information Sciences 2024
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693) - Yan et al., WWW 2024
- [GFedKG: GNN-based Federated Embedding Model for Knowledge Graph Completion](https://www.sciencedirect.com/science/article/abs/pii/S0950705124009249) - Wang et al., KBS 2024

**Decentralized Aggregation (Models)**
- **[Distributed Training of Graph Convolutional Networks](https://doi.org/10.1109/TSIPN.2020.3046237)** - Scardapane et al., TSIPN 2020 🌟
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://dl.acm.org/doi/10.1145/3583780.3614834) - Zheng et al., CIKM 2023
- [Fedgs: Federated Graph-Based Sampling with Arbitrary Client Availability](https://doi.org/10.1609/aaai.v37i8.26223) - Wang et al., AAAI 2023
- [HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660) - Guo et al., KDD 2024
- [A Graph Neural Network Based Decentralized Learning Scheme](https://doi.org/10.3390/s22031030) - Gao et al., Sensors 2022
- [Fully-Decentralized Training of GNNs Using Layer-Wise Self-Supervision](https://arxiv.org/abs/2302.08691) - Giaretta & Girdzijauskas, 2023
- [Tackling Resource Allocation for Decentralized Federated Learning: A GNN-based Approach](https://doi.org/10.1109/TMC.2025.3562834) - Meng et al., TMC 2025
- **[Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://doi.org/10.48550/arXiv.2509.08409)** - Wang et al., 2025 🌟
- **[Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://doi.org/10.48550/arXiv.2412.06105)** - Olshevskyi et al., ICMLCN 2025 🌟
- **[Spreadgnn: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data](https://doi.org/10.1609/aaai.v36i6.20643)** - He et al., AAAI 2022 🌟

**Decentralized (Embeddings)**
- **[A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-based Policies](https://doi.org/10.1109/ICRA46639.2022.9811744)** - Blumenkamp et al., ICRA 2022 🌟
- **[GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://doi.org/10.48550/arXiv.2210.17281)** - Zeng et al., JSAC 2022 🌟
- [Federated Learning over Coupled Graphs](https://doi.org/10.1109/TPDS.2023.3240527) - Lei et al., TPDS 2023
- [Fedgs: Federated Graph-Based Sampling with Arbitrary Client Availability](https://doi.org/10.1609/aaai.v37i8.26223) - Wang et al., AAAI 2023
- [Semi-Decentralized Federated Ego Graph Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3543507.3583337) - Qu et al., WWW 2023
- [HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660) - Guo et al., KDD 2024
- [Stochastic Graph Neural Networks](https://doi.org/10.1109/TSP.2021.3092336) - Gao et al., TSP 2021
- [Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection](https://doi.org/10.48550/arXiv.2105.08601) - Zhou et al., 2021
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://arxiv.org/abs/2104.09027) - Lee et al., TMC 2021
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-based Policies](https://doi.org/10.1109/ICRA46639.2022.9811744) - Blumenkamp et al., ICRA 2022
- [Fedwalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy](https://doi.org/10.1145/3534678.3539308) - Pan et al., KDD 2022
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://doi.org/10.1109/TSP.2022.3163626) - Wang et al., TSP 2022
- [Fully-Decentralized Training of GNNs Using Layer-Wise Self-Supervision](https://arxiv.org/abs/2302.08691) - Giaretta & Girdzijauskas, 2023
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://doi.org/10.1109/TWC.2023.3279442) - Lee et al., TWC 2023
- [Graph Neural Networks Gone Hogwild](https://openreview.net/forum?id=WfxPVtYRlL) - Solodova et al., ICLR 2025
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- **[Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://doi.org/10.1109/TSP.2025.3534685)** - Gao & Gündüz, TSP 2025 🌟
- **[Airgnns: Graph Neural Networks over the Air](https://doi.org/10.23919/EUSIPCO58844.2023.10289977)** - Gao & Gündüz, EUSIPCO 2023 🌟
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://doi.org/10.1109/ACCESS.2022.3159688) - Krasanakis et al., IEEE Access 2022
- [Decentralized Channel Management in WLANs with Graph Neural Networks](https://doi.org/10.1109/ICC45041.2023.10279331) - Gao et al., ICC 2023
- [Semi-Decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://doi.org/10.1109/TVT.2024.3355971) - Nazzal et al., TVT 2024
- **[Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://doi.org/10.48550/arXiv.2412.06105)** - Olshevskyi et al., ICMLCN 2025 🌟
- [Tackling Resource Allocation for Decentralized Federated Learning: A GNN-based Approach](https://doi.org/10.1109/TMC.2025.3562834) - Meng et al., TMC 2025
- **[Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://doi.org/10.48550/arXiv.2509.08409)** - Wang et al., 2025 🌟
- **[Disease Prediction via Graph Neural Networks](https://doi.org/10.1109/JBHI.2020.3004143)** - Sun et al., JBHI 2020 🌟

**Hierarchical (Models)**
- **[HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660)** - Guo et al., KDD 2024 🌟

**Hierarchical (Embeddings)**
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- **[Semi-Decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://doi.org/10.1109/TVT.2024.3355971)** - Nazzal et al., TVT 2024 🌟

#### Asynchronicity

**Stale Message Passing**
- **[GwAC: GNNs with Asynchronous Communication](https://proceedings.mlr.press/v231/faber24a.html)** - Faber & Wattenhofer, LoG 2024 🌟
- **[Graph Neural Networks Gone Hogwild](https://openreview.net/forum?id=WfxPVtYRlL)** - Solodova et al., ICLR 2025 🌟
- [Rethinking Message Passing for Algorithmic Alignment](https://openreview.net/forum?id=06V4SHkhMn) - Mathys et al., NeurIPS 2024
- [A Graph Neural Network Based Decentralized Learning Scheme](https://doi.org/10.3390/s22031030) - Gao et al., Sensors 2022
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://doi.org/10.1109/TSP.2022.3163626) - Wang et al., TSP 2022

#### Communication

**Transmission Efficiency**
- **[Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://arxiv.org/abs/2104.09027)** - Lee et al., TMC 2021 🌟
- [Stochastic Graph Neural Networks](https://doi.org/10.1109/TSP.2021.3092336) - Gao et al., TSP 2021
- **[Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://doi.org/10.48550/arXiv.2509.08409)** - Wang et al., 2025 🌟

**Wireless Communication for Aggregation**
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-based Policies](https://doi.org/10.1109/ICRA46639.2022.9811744) - Blumenkamp et al., ICRA 2022
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://doi.org/10.1109/TSP.2022.3163626) - Wang et al., TSP 2022
- **[Airgnns: Graph Neural Networks over the Air](https://doi.org/10.23919/EUSIPCO58844.2023.10289977)** - Gao & Gündüz, EUSIPCO 2023 🌟
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://doi.org/10.1109/TWC.2023.3279442) - Lee et al., TWC 2023
- **[Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://doi.org/10.1109/TSP.2025.3534685)** - Gao & Gündüz, TSP 2025 🌟
- **[Topological Neural Networks over the Air](https://doi.org/10.1109/ICASSP48485.2024.10446693)** - Fiorellino et al., ICASSP 2024 🌟

**Client Scheduling and Topology Management**
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://doi.org/10.48550/arXiv.2210.17281) - Zeng et al., JSAC 2022
- [Semi-Decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://doi.org/10.1109/TVT.2024.3355971) - Nazzal et al., TVT 2024
- [Distributed Training of Graph Convolutional Networks](https://doi.org/10.1109/TSIPN.2020.3046237) - Scardapane et al., TSIPN 2020
- **[Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://doi.org/10.48550/arXiv.2509.08409)** - Wang et al., 2025 🌟

---

### Privacy-Preserving Techniques

**For Model Parameters**
- [FedGNC: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](https://proceedings.neurips.cc) - Yao et al., NeurIPS 2023
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9) - Wu et al., Nature Communications 2022
- [Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693) - Yan et al., WWW 2024

**For Exchanged Embeddings**
- [Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs](https://ieeexplore.ieee.org/document/10305592/) - Pei et al., TIFS 2023
- [Towards Private Learning on Decentralized Graphs with Local Differential Privacy](https://ieeexplore.ieee.org/document/9855440/) - Lin et al., TIFS 2022
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://doi.org/10.1109/TWC.2023.3279442) - Lee et al., TWC 2023
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://doi.org/10.24963/ijcai.2022/272) - Chen et al., IJCAI 2021
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://doi.org/10.48550/arXiv.2106.11593) - Ni et al., 2021
- [Adversarial Attacks on GNN-Based Vertical Federated Learning](https://doi.org/10.1016/j.cose.2023.103601) - Chen et al., Springer 2024
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- **[A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9)** - Wu et al., Nature Communications 2022 🌟
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14) - Qiu et al., KSEM 2022
- **[Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693)** - Yan et al., WWW 2024 🌟
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://dl.acm.org/doi/10.1145/3583780.3614834) - Zheng et al., CIKM 2023
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023

**For Topology**
- [Fedgraph: Federated Graph Learning with Intelligent Sampling](https://ieeexplore.ieee.org/document/9606516) - Chen et al., TPDS 2021
- [HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660) - Guo et al., KDD 2024
- [Privacy-Enhanced Federated GNN Inference Against Adversarial Example Attack](https://doi.org/10.1109/TETCI.2024.3502434) - He et al., TETCI 2024
- [Towards Private Learning on Decentralized Graphs with Local Differential Privacy](https://ieeexplore.ieee.org/document/9855440/) - Lin et al., TIFS 2022
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- [Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs](https://ieeexplore.ieee.org/document/10305592/) - Pei et al., TIFS 2023
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://doi.org/10.48550/arXiv.2106.11593) - Ni et al., 2021
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- [A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9) - Wu et al., Nature Communications 2022
- **[A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14)** - Qiu et al., KSEM 2022 🌟
- **[Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693)** - Yan et al., WWW 2024 🌟
- [Subgraph-Level Federated Graph Neural Network for Privacy-Preserving Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0893608024004982) - Han et al., Neural Networks 2024
- **[No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950)** - Agrawal et al., AAAI 2024 🌟
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [Efficient Federated Learning on Knowledge Graphs via Privacy-Preserving Relation Embedding Aggregation](https://aclanthology.org/2022.findings-emnlp.43) - Zhang et al., ACL 2022
- [FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting](https://doi.org/10.1109/TII.2021.3055283) - Zhang et al., TII 2021
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023

---

## Organized by Graph Partitioning

![Graph Partitioning](assets/mgi_vs_sgi.png)

Collaborative learning on graph data can be broadly categorized into two settings: (a) agents holding multiple graph instances, and (b) agents holding subgraphs.

### Subgraph - Homogeneous

![Homogeneous Graph Partitioning](assets/homogeneous_graph.png)

**Illustration of different subgraph partitioning schemes in a homogeneous graph.** In *topology partitioning* (a), the graph topology is divided across agents by edge cuts, with each agent observing complete node features for assigned nodes. Two cases exist: *ego-graph partition* (i), where an agent observes a node with its direct neighbors; and *subgraph partition* (ii), where an agent holds a larger portion with multiple aligned nodes. In *feature partitioning* (b), agents share the same nodes but access different feature subsets: *full topology knowledge* (i), where all agents know complete graph links; and *topology-free* (ii), where only a subset of agents has link information.

#### Horizontal Feature Partition

**Subgraph Granularity (Edge Cut)**

*Representative Work:*
- 🌟 **[Subgraph Federated Learning with Missing Neighbor Generation](https://arxiv.org/abs/2106.13430)** - Zhang et al., NeurIPS 2021

*Other Works:*
- [Fedgraph: Federated Graph Learning with Intelligent Sampling](https://ieeexplore.ieee.org/document/9606516) - Chen et al., TPDS 2021
- [Distributed Training of Graph Convolutional Networks](https://doi.org/10.1109/TSIPN.2020.3046237) - Scardapane et al., TSIPN 2020
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://dl.acm.org/doi/10.1145/3583780.3614834) - Zheng et al., CIKM 2023
- [Fedgs: Federated Graph-Based Sampling with Arbitrary Client Availability](https://doi.org/10.1609/aaai.v37i8.26223) - Wang et al., AAAI 2023
- [HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660) - Guo et al., KDD 2024
- [A Graph Neural Network Based Decentralized Learning Scheme](https://doi.org/10.3390/s22031030) - Gao et al., Sensors 2022
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-based Policies](https://doi.org/10.1109/ICRA46639.2022.9811744) - Blumenkamp et al., ICRA 2022
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://doi.org/10.48550/arXiv.2210.17281) - Zeng et al., JSAC 2022
- [Federated Learning over Coupled Graphs](https://doi.org/10.1109/TPDS.2023.3240527) - Lei et al., TPDS 2023
- [Fedgs: Federated Graph-Based Sampling with Arbitrary Client Availability](https://doi.org/10.1609/aaai.v37i8.26223) - Wang et al., AAAI 2023
- [Semi-Decentralized Federated Ego Graph Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3543507.3583337) - Qu et al., WWW 2023
- [HiFGL: A Hierarchical Framework for Cross-Silo Cross-Device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660) - Guo et al., KDD 2024
- [FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](http://papers.nips.cc/paper_files/paper/2023/hash/fc07feae9af49dd3f1a1e049b77f4e17-Abstract-Conference.html) - Yao et al., NeurIPS 2023
- [Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs](https://ieeexplore.ieee.org/document/10305592/) - Pei et al., TIFS 2023
- [Fedsheafhn: Personalized Federated Learning on Graph-Structured Data](https://doi.org/10.48550/arXiv.2405.16056) - Liang et al., 2024
- [Fedni: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://ieeexplore.ieee.org/document/9815303/) - Peng et al., TMI 2022

**Ego Granularity (Edge Cut)**

*Representative Work:*
- 🌟 **[Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://arxiv.org/abs/2509.08409)** - Wang et al., 2025

*Other Works:*
- [Federated Graph Learning with Periodic Neighbour Sampling](https://doi.org/10.1109/IWQoS54832.2022.9812908) - Du et al., IWQoS 2022
- [Stochastic Graph Neural Networks](https://doi.org/10.1109/TSP.2021.3092336) - Gao et al., TSP 2021
- [Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection](https://doi.org/10.48550/arXiv.2105.08601) - Zhou et al., 2021
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://arxiv.org/abs/2104.09027) - Lee et al., TMC 2021
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-based Policies](https://doi.org/10.1109/ICRA46639.2022.9811744) - Blumenkamp et al., ICRA 2022
- [Fedwalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy](https://doi.org/10.1145/3534678.3539308) - Pan et al., KDD 2022
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://doi.org/10.1109/TSP.2022.3163626) - Wang et al., TSP 2022
- [Fully-Decentralized Training of GNNs Using Layer-Wise Self-Supervision](https://arxiv.org/abs/2302.08691) - Giaretta & Girdzijauskas, 2023
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://doi.org/10.1109/TWC.2023.3279442) - Lee et al., TWC 2023
- [Graph Neural Networks Gone Hogwild](https://openreview.net/forum?id=WfxPVtYRlL) - Solodova et al., ICLR 2025
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- **[Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://doi.org/10.1109/TSP.2025.3534685)** - Gao & Gündüz, TSP 2025
- **[Airgnns: Graph Neural Networks over the Air](https://doi.org/10.23919/EUSIPCO58844.2023.10289977)** - Gao & Gündüz, EUSIPCO 2023
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://doi.org/10.1109/ACCESS.2022.3159688) - Krasanakis et al., IEEE Access 2022
- [Decentralized Channel Management in WLANs with Graph Neural Networks](https://doi.org/10.1109/ICC45041.2023.10279331) - Gao et al., ICC 2023
- [Semi-Decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://doi.org/10.1109/TVT.2024.3355971) - Nazzal et al., TVT 2024
- **[Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://doi.org/10.48550/arXiv.2412.06105)** - Olshevskyi et al., ICMLCN 2025
- [Tackling Resource Allocation for Decentralized Federated Learning: A GNN-based Approach](https://doi.org/10.1109/TMC.2025.3562834) - Meng et al., TMC 2025
- [Lumos: Heterogeneity-Aware Federated Graph Learning over Decentralized Devices](https://doi.org/10.1109/ICDE55515.2023.00149) - Pan et al., ICDE 2023
- [Semi-Decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://doi.org/10.1109/TVT.2024.3355971) - Nazzal et al., TVT 2024
- [GwAC: GNNs with Asynchronous Communication](https://proceedings.mlr.press/v231/faber24a.html) - Faber & Wattenhofer, LoG 2024
- [Graph Neural Networks Gone Hogwild](https://openreview.net/forum?id=WfxPVtYRlL) - Solodova et al., ICLR 2025
- [Rethinking Message Passing for Algorithmic Alignment](https://openreview.net/forum?id=06V4SHkhMn) - Mathys et al., NeurIPS 2024
- [A Graph Neural Network Based Decentralized Learning Scheme](https://doi.org/10.3390/s22031030) - Gao et al., Sensors 2022
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://doi.org/10.1109/TSP.2022.3163626) - Wang et al., TSP 2022
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://arxiv.org/abs/2104.09027) - Lee et al., TMC 2021
- [Stochastic Graph Neural Networks](https://doi.org/10.1109/TSP.2021.3092336) - Gao et al., TSP 2021
- [Towards Private Learning on Decentralized Graphs with Local Differential Privacy](https://ieeexplore.ieee.org/document/9855440/) - Lin et al., TIFS 2022
- [Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling](https://doi.org/10.1145/3447548.3467371) - Meng et al., KDD 2021
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://doi.org/10.1109/ACCESS.2022.3159688) - Krasanakis et al., IEEE Access 2022

#### Vertical Feature Partition

**Edge-Full Granularity (Vertex Cut)**

*Representative Work:*
- 🌟 **[A Vertical Federated Learning Framework for Graph Convolutional Network](https://arxiv.org/abs/2106.11593)** - Ni et al., 2021

*Other Works:*
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://doi.org/10.24963/ijcai.2022/272) - Chen et al., IJCAI 2021
- [Adversarial Attacks on GNN-Based Vertical Federated Learning](https://doi.org/10.1016/j.cose.2023.103601) - Chen et al., Springer 2024
- [Federated Graph Learning with Graphless Clients](https://openreview.net/forum?id=mVAp0eDfyR) - Fu et al., TMLR 2024
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022

**Edge-Free Granularity (Vertex Cut)**

*Representative Work:*
- 🌟 **[Fedsgc: Federated Simple Graph Convolution for Node Classification](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf)** - Cheung et al., FTL-IJCAI 2021

*Other Works:*
- [Federated Graph Neural Network for Cross-Graph Node Classification](https://doi.org/10.1109/CCIS53392.2021.9754598) - Guan et al., CCIS 2021
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024

### Subgraph - Heterogeneous

![Heterogeneous Graph Partitioning](assets/heterogeneous_graph.png)

**Illustration of different subgraph partitioning schemes in a heterogeneous graph.** The subgraph can be partitioned at two levels: either by dividing the node set across agents, or by partitioning the schema graph, distributing the object types across agents.

#### Horizontal Feature Partition - No Type Partition

**Subgraph Granularity (Vertex Cut)**
- [Subgraph-Level Federated Graph Neural Network for Privacy-Preserving Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0893608024004982) - Han et al., Neural Networks 2024

**Ego Granularity (Vertex Cut)**
- [Fede: Embedding Knowledge Graphs in Federated Setting](https://dl.acm.org/doi/abs/10.1145/3502223.3502233) - Chen et al., IJCKG 2021
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [Federated Social Recommendation with Graph Neural Network](https://dl.acm.org/doi/10.1145/3501815) - Liu et al., TIST 2022
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14) - Qiu et al., KSEM 2022
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://www.sciencedirect.com/science/article/abs/pii/S002002552301561X) - Chen et al., Information Sciences 2024
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021
- [Fedgnn: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.48550/arXiv.2102.04925) - Wu et al., 2021
- [A Federated Graph Neural Network Framework for Privacy-Preserving Personalization](https://doi.org/10.1038/s41467-022-30714-9) - Wu et al., Nature Communications 2022
- [Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling](https://doi.org/10.1109/IWQOS52092.2021.9521331) - Liu et al., IWQoS 2021
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://www.sciencedirect.com/science/article/abs/pii/S002002552301561X) - Chen et al., Information Sciences 2024
- [No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/28950) - Agrawal et al., AAAI 2024
- [Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3589334.3645693) - Yan et al., WWW 2024
- [GFedKG: GNN-based Federated Embedding Model for Knowledge Graph Completion](https://www.sciencedirect.com/science/article/abs/pii/S0950705124009249) - Wang et al., KBS 2024

#### Horizontal Feature Partition - With Type Partition

**Subgraph Granularity (Vertex Cut)**
- [FGTL: Federated Graph Transfer Learning for Node Classification](https://dl.acm.org/doi/10.1145/3699962) - Mai et al., TKDD 2024
- [Federated Knowledge Graph Completion via Embedding-Contrastive Learning](https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316) - Chen et al., KBS 2022
- [Differentially Private Federated Knowledge Graphs Embedding](https://dl.acm.org/doi/10.1145/3459637.3482252) - Peng et al., CIKM 2021

**Ego Granularity (Vertex Cut)**
- **[Disease Prediction via Graph Neural Networks](https://doi.org/10.1109/JBHI.2020.3004143)** - Sun et al., JBHI 2020 🌟

### Multiple Graph Instances

![Multiple Graph Instances Partitioning](assets/mgi_partition.png)

**Illustration of different partitioning schemes for multiple graph instances.** Multiple graph instances can be partitioned (a) horizontally, where agents hold unrelated graph samples, or (b) vertically, where graph instances associated with the same label are divided among agents.

#### Homogeneous or Heterogeneous (No Partitioning)

*Representative Work:*
- 🌟 **[Spreadgnn: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data](https://doi.org/10.1609/aaai.v36i6.20643)** - He et al., AAAI 2022

*Other Works:*
- [Graphfl: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://ieeexplore.ieee.org/document/10027604/) - Wang et al., ICDM 2022
- [FedEgo: Privacy-Preserving Personalized Federated Graph Learning with Ego-Graphs](https://dl.acm.org/doi/10.1145/3624017) - Zhang et al., TKDD 2023
- **[Personalized Subgraph Federated Learning](https://proceedings.mlr.press/v202/baek23a.html)** - Baek et al., ICML 2023
- [Subgraph Federated Learning for Local Generalization](https://arxiv.org/abs/2503.03995) - Kim et al., ICLR 2025
- [Federated Graph Classification over Non-Iid Graphs](https://proceedings.neurips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html) - Xie et al., NeurIPS 2021
- [Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning](https://doi.org/10.1609/aaai.v39i16.33814) - Fang et al., AAAI 2025
- **[EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification](https://openreview.net/forum?id=Bd9JlrqZhN)** - Shi et al., ICML 2025
- [An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning](https://link.springer.com/chapter/10.1007/978-3-031-30637-2_50) - Guo et al., DASFAA 2023
- [BI-FedGNN: Federated Graph Neural Networks Framework Based on Bayesian Inference](https://www.sciencedirect.com/science/article/abs/pii/S0893608023005786) - Gao et al., Neural Networks 2024
- [FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling](https://arxiv.org/abs/2501.12592) - Ceyani et al., SDM 2025
- **[FedSPA: Generalizable Federated Graph Learning Under Homophily Heterogeneity](https://doi.org/10.1109/CVPR52734.2025.01441)** - Tan et al., CVPR 2025
- **[Fedsgc: Federated Simple Graph Convolution for Node Classification](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf)** - Cheung et al., FTL-IJCAI 2021

#### Heterogeneous (Vertical Partitioning)

*Representative Work:*
- 🌟 **[Semi-Supervised Graph Instance Transformer for Mental Health Inference](https://doi.org/10.1109/ICMLA52953.2021.00198)** - Dong et al., ICMLA 2021

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## Citation

If you find this repository useful, please cite our survey paper:

```bibtex
@article{anonymous2026euclidean,
  title={From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning},
  author={Anonymous authors},
  journal={Submitted to Transactions on Machine Learning Research (TMLR)},
  year={2024},
  month={November},
  url={https://openreview.net/forum?id=vj9l8AjLT6&noteId=vj9l8AjLT6},
  note={Submission Number: 6504}
}
```

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## Contributing

We welcome contributions! If you know of any relevant papers that should be included, please kindly send an email at [...]

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