Summary of Openfgl: a Comprehensive Benchmark For Federated Graph Learning, by Xunkai Li et al.
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by Xunkai Li, Yinlin Zhu, Boyang Pang, Guochen Yan, Yeyu Yan, Zening Li, Zhengyu Wu, Wentao Zhang, Rong-Hua Li, Guoren Wang
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Social and Information Networks (cs.SI)
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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 OpenFGL, a unified benchmark for federated graph learning (FGL) that addresses the lack of fair evaluation methods for various real-world applications. FGL is a distributed training paradigm for graph neural networks that involves large-scale processing without direct data sharing. The authors identify three primary scenarios: Graph-FL and Subgraph-FL, which pose different challenges. OpenFGL includes 42 graph datasets from 18 domains, 8 simulation strategies emphasizing various graph properties, and 5 downstream tasks. Additionally, it offers 18 state-of-the-art FGL algorithms through a user-friendly API for thorough comparison and evaluation of effectiveness, robustness, and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called OpenFGL to help people compare different ways of learning on graphs without sharing their data. Graphs are like maps that show connections between things. Right now, there isn’t a good way to test which method works best for different types of graph problems. This new tool will make it easier for researchers to try out different methods and see how well they work. |
