<commit_before>---
title: "FedSGC: Federated Simple Graph Convolution for Node Classification"
collection: publications
permalink: /publication/2021-07-20-fedsgc-2
excerpt: ''
date: 2021-07-20
venue: 'International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021 (FTL-IJCAI'21)'
paperurl: 'https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf'
code: ''
---

Graph Neural Networks (GNN) have developed rapidly and solved a wide range of graph-related tasks. One advantage of GNN over traditional neu- ral networks is the utilization of neighbouring in- formation. However, under the increasing concern in data privacy, accessing raw information from dif- ferent parties may raise privacy concerns. To ad- dress this, federated learning, as a distributed learn- ing mechanism, is proposed to train models with decentralized data owned by different data parties without sharing or leaking the raw data. In this work, we study the vertical and horizontal settings for federated learning on graph data. We pro- pose FedSGC to train the Simple Graph Convo- lution model under three data split scenarios. We also demonstrate that the prediction performance of FedSGC is closely aligned with the non-federated model trained in centralized manner.

[pdf]()v
[code]()<commit_msg>2022-oct-3<commit_after>---
title: "FedSGC: Federated Simple Graph Convolution for Node Classification"
collection: publications
permalink: /publication/2021-07-20-fedsgc-2
excerpt: ''
date: 2021-07-20
venue: 'International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021 (FTL-IJCAI'21)'
paperurl: 'https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf'
code: ''
---

Graph Neural Networks (GNN) have developed rapidly and solved a wide range of graph-related tasks. One advantage of GNN over traditional neu- ral networks is the utilization of neighbouring in- formation. However, under the increasing concern in data privacy, accessing raw information from dif- ferent parties may raise privacy concerns. To ad- dress this, federated learning, as a distributed learn- ing mechanism, is proposed to train models with decentralized data owned by different data parties without sharing or leaking the raw data. In this work, we study the vertical and horizontal settings for federated learning on graph data. We pro- pose FedSGC to train the Simple Graph Convo- lution model under three data split scenarios. We also demonstrate that the prediction performance of FedSGC is closely aligned with the non-federated model trained in centralized manner.

[pdf](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf)
[code]()
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