Summary of Fedgraph: a Research Library and Benchmark For Federated Graph Learning, by Yuhang Yao et al.
FedGraph: A Research Library and Benchmark for Federated Graph Learning
by Yuhang Yao, Yuan Li, Xinyi Fan, Junhao Li, Kay Liu, Weizhao Jin, Srivatsan Ravi, Philip S. Yu, Carlee Joe-Wong
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces FedGraph, a research library designed for practical distributed deployment and benchmarking in federated graph learning. The library supports state-of-the-art graph learning methods and includes profiling tools to evaluate system performance, focusing on communication and computation costs during training. FedGraph also incorporates homomorphic encryption for privacy preservation and enables distributed training across multiple machines, providing an evaluation framework for future algorithms. The paper demonstrates the first privacy-preserving federated learning system on a graph with 100 million nodes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated graph learning is a way to train machine learning models on big graphs without sharing sensitive data. Many algorithms have been developed to make this work better, but it’s hard to compare them because their performance is not well understood. The authors of this paper created a library called FedGraph that helps solve this problem by providing tools for training and evaluating these models in a way that takes into account the costs of communication and computation. This library also includes ways to keep data private using homomorphic encryption, which makes it possible to train models on sensitive data without seeing the data itself. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
