Authors
- Chaochao Chen*
- Longfei Zheng*
- Huiwen Wu*
- Lingjuan Lyu
- Jun Zhou*
- Jia Wu*
- Bingzhe Wu*
- Ziqi Liu*
- Li Wang*
- Xiaolin Zheng*
* External authors
Venue
- IJCAI 2022
Date
- 2022
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen*
Longfei Zheng*
Huiwen Wu*
Jun Zhou*
Jia Wu*
Bingzhe Wu*
Ziqi Liu*
Li Wang*
Xiaolin Zheng*
* External authors
IJCAI 2022
2022
Abstract
Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data. High-performance GNN models always depend on both rich features and complete edge information in graph. However, such information could possibly be isolated by different data holders in practice, which is the so-called data isolation problem. To solve this problem, in this paper, we propose Vertically Federated Graph Neural Network (VFGNN), a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. We conduct experiments on three benchmarks and the results demonstrate the effectiveness of VFGNN.
Related Publications
In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non watermarked dataset as a ref…
Current image anonymization techniques, largely focus on localized pseudonymization, typically modify identifiable features like faces or full bodies and evaluate anonymity through metrics such as detection and re-identification rates. However, this approach often overlooks …
We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize as task, data, and model levels. At the task level, COALA extends support from simple classification to 15 computer vision tasks, in…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.