Graph Privacy: A Heterogeneous Federated GNN for Trans-Border Financial Data Circulation
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Abstract (arXiv)
The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharing, to ensure that the data is available but not visible, to realize the joint portrait of all kinds of heterogeneous data of business organizations in different industries, we propose a Heterogeneous Federated Graph Neural Network (HFGNN) approach. In this method, the distribution of heterogeneous business data of trans-border organizations is taken as subgraphs, and the sharing and circulation process among subgraphs is constructed as a statistically heterogeneous global graph through a central server. Each subgraph learns the corresponding personalized service model through local training to select and update the relevant subset of subgraphs with aggregated parameters, and effectively separates and combines topological and feature information among subgraphs. Finally, our simulation experimental results show that the proposed method has higher accuracy performance and faster convergence speed than existing methods.
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2020LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacycited by 64
- 2020Local Differential Privacy and Its Applications: A Comprehensive Surveycited by 41
- 2022FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networkscited by 23
- 2023Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendationcited by 19
- 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 10
- 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
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