摘要
Abstract
Federated graph neural networks combine graph machine learning with federated learning,allowing par-ties to collaborate on training models without sharing private graph data.However,due to the partitioning of graph data into multiple clients during training,the existence of cross-client edges may result in node neighbor information being stored on different devices,leading to information loss.Meanwhile,the subgraphs held by each client often come from different parts of the global graph(such as different communities),resulting in significant heterogeneity in data distribution.To address the two major challenges of missing cross-client edge information and data heteroge-neity in distributed graph data,a federated graph convolutional neural network training framework was proposed.This framework used homomorphic encryption based secure cross-client neighbor feature aggregation in the pre-training round to compensate for local structural information gaps;An attention federated aggregation mechanism based on random graph embedding was designed and used in the training round,which dynamically evaluates client similarity to achieve intelligent weight allocation and avoid forced fusion of heterogeneous data.At the same time,by integrating adaptive sparse masking technology,personalized filtering and efficient updating are achieved at the pa-rameter level.The experimental results on the Cora,CiteSeer and PubMed datasets show that the proposed framework achieves an accuracy improvement of up to 11.58%over the mainstream baselines in the node classification task.关键词
联邦学习/图神经网络/联邦图神经网络/同态加密Key words
federated learning/graph neural network/federated graph neural network/homomorphic encryption分类
信息技术与安全科学
