Summary of A Federated Parameter Aggregation Method For Node Classification Tasks with Different Graph Network Structures, by Hao Song et al.
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
by Hao Song, Jiacheng Yao, Zhengxi Li, Shaocong Xu, Shibo Jin, Jiajun Zhou, Chenbo Fu, Qi Xuan, Shanqing Yu
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel federated learning approach for graph neural networks, called FLGNN, is proposed to address the challenges of aggregating model gradients across heterogeneous graphs. This method enables collaborative training on multiple sources without compromising privacy. The paper investigates the effectiveness of FLGNN by experimenting with real-world datasets and verifies its robustness against membership inference attacks. Additionally, differential privacy defense experiments demonstrate the success rate of privacy theft can be further reduced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way to train graph neural networks together without sharing sensitive data. They created a method called FLGNN that works well for different types of graphs and keeps personal information safe. The team tested FLGNN with real-world data sets and showed it can resist attempts to figure out which users contributed data. |
Keywords
* Artificial intelligence * Federated learning * Inference
