FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data

Kai Hu, Jiasheng Wu, Yaogen Li, Meixia Lu, Liguo Weng and Min Xia
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Kai Hu: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiasheng Wu: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yaogen Li: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Meixia Lu: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Liguo Weng: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Min Xia: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China

Mathematics, 2022, vol. 10, issue 6, 1-24

Abstract: Federated Learning (FL) can combine multiple clients for training and keep client data local, which is a good way to protect data privacy. There are many excellent FL algorithms. However, most of these can only process data with regular structures, such as images and videos. They cannot process non-Euclidean spatial data, that is, irregular data. To address this problem, we propose a Federated Learning-Based Graph Convolutional Network (FedGCN). First, we propose a Graph Convolutional Network (GCN) as a local model of FL. Based on the classical graph convolutional neural network, TopK pooling layers and full connection layers are added to this model to improve the feature extraction ability. Furthermore, to prevent pooling layers from losing information, cross-layer fusion is used in the GCN, giving FL an excellent ability to process non-Euclidean spatial data. Second, in this paper, a federated aggregation algorithm based on an online adjustable attention mechanism is proposed. The trainable parameter ρ is introduced into the attention mechanism. The aggregation method assigns the corresponding attention coefficient to each local model, which reduces the damage caused by the inefficient local model parameters to the global model and improves the fault tolerance and accuracy of the FL algorithm. Finally, we conduct experiments on six non-Euclidean spatial datasets to verify that the proposed algorithm not only has good accuracy but also has a certain degree of generality. The proposed algorithm can also perform well in different graph neural networks.

Keywords: federated learning; graph convolutional neural network; non-Euclidean spatial data; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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