Summary of Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning, by Xingbo Fu et al.
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
by Xingbo Fu, Zihan Chen, Yinhan He, Song Wang, Binchi Zhang, Chen Chen, Jundong Li
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper proposes Federated Graph Learning (FGL) framework, entitled FedVN, to jointly train powerful graph learning models like Graph Neural Networks (GNNs) without sharing local graph data. The FGL enables multiple clients to collaborate on graph-related downstream tasks such as graph property prediction while addressing the issue of distribution shifts in graph data across clients. To tackle this challenge, FedVN incorporates client-specific graph augmentation strategies with multiple learnable Virtual Nodes (VNs). Each client trains a personalized edge generator that determines how VNs connect local graphs in a client-specific manner. Theoretical analyses demonstrate that FedVN can eliminate distribution shifts of graph data, and experimental results on four datasets under five settings show the superiority of FedVN over nine baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Graph Learning (FGL) is a way for multiple clients to work together on graph-related tasks without sharing their private data. The problem is that the graph data can be very different from one client to another, which makes it hard to train good models. To solve this, researchers propose a new FGL framework called FedVN. It uses special “virtual nodes” that are learned by each client and helps connect their graphs in a way that makes sense for them. This approach is better than other methods at eliminating the differences between the clients’ data and improves performance on various tasks. |
