# Graph Privacy: A Heterogeneous Federated GNN for Trans-Border Financial Data Circulation

arXiv: https://arxiv.org/abs/2505.00257 · HTML: https://mlsanity.com/paper/2505.00257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2007.15789/) (2020)
- [Local Differential Privacy and Its Applications: A Comprehensive Survey](https://mlsanity.com/paper/2008.03686/) (2020)
- [FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks](https://mlsanity.com/paper/2210.14226/) (2022)
- [Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation](https://mlsanity.com/paper/2310.11730/) (2023)
- [FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation](https://mlsanity.com/paper/2301.12623/) (2023)
- [Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation](https://mlsanity.com/paper/2401.15874/) (2024)

## 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.