# GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs

arXiv: https://arxiv.org/abs/2012.04187 · HTML: https://mlsanity.com/paper/2012.04187/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 30 of 64)

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [Heterogeneous Graph Masked Autoencoders](https://mlsanity.com/paper/2208.09957/) (2022)
- [GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation](https://mlsanity.com/paper/2203.00949/) (2022)
- [Cluster-driven Graph Federated Learning over Multiple Domains](https://mlsanity.com/paper/2104.14628/) (2021)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://mlsanity.com/paper/2105.03170/) (2021)
- [Multimodal Federated Learning on IoT Data](https://mlsanity.com/paper/2109.04833/) (2021)
- [SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2106.02743/) (2021)
- [Personalized Federated Learning With Graph](https://mlsanity.com/paper/2203.00829/) (2022)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](https://mlsanity.com/paper/2201.12433/) (2022)
- [Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck](https://mlsanity.com/paper/2408.00295/) (2024)
- [A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection](https://mlsanity.com/paper/2205.10014/) (2022)
- [Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting](https://mlsanity.com/paper/2109.07258/) (2021)
- [Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning](https://mlsanity.com/paper/2110.06468/) (2021)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs](https://mlsanity.com/paper/2208.13685/) (2022)
- [Federated Learning over Coupled Graphs](https://mlsanity.com/paper/2301.11099/) (2023)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey](https://mlsanity.com/paper/2307.04338/) (2023)
- [FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy](https://mlsanity.com/paper/2205.15896/) (2022)

## Abstract (arXiv)

Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing centralized GraphSSC methods are impractical to solve many real-world graph-based problems, as collecting the entire graph and labeling a reasonable number of labels is time-consuming and costly, and data privacy may be also violated. Federated learning (FL) is an emerging learning paradigm that enables collaborative learning among multiple clients, which can mitigate the issue of label scarcity and protect data privacy as well. Therefore, performing GraphSSC under the FL setting is a promising solution to solve real-world graph-based problems. However, existing FL methods 1) perform poorly when data across clients are non-IID, 2) cannot handle data with new label domains, and 3) cannot leverage unlabeled data, while all these issues naturally happen in real-world graph-based problems. To address the above issues, we propose the first FL framework, namely GraphFL, for semi-supervised node classification on graphs. Our framework is motivated by meta-learning methods. Specifically, we propose two GraphFL methods to respectively address the non-IID issue in graph data and handle the tasks with new label domains. Furthermore, we design a self-training method to leverage unlabeled graph data. We adopt representative graph neural networks as GraphSSC methods and evaluate GraphFL on multiple graph datasets. Experimental results demonstrate that GraphFL significantly outperforms the compared FL baseline and GraphFL with self-training can obtain better performance.