Summary of Communication-efficient Personalized Federal Graph Learning Via Low-rank Decomposition, by Ruyue Liu et al.
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by Ruyue Liu, Rong Yin, Xiangzhen Bo, Xiaoshuai Hao, Xingrui Zhou, Yong Liu, Can Ma, Weiping Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A new federated graph learning algorithm called CEFGL is proposed to address challenges in processing private graph data across heterogeneous clients while maintaining privacy. The method decomposes model parameters into low-rank generic and sparse private models, allowing for personalized and shared knowledge representations. Local stochastic gradient descent iterations are performed between communication phases, and efficient compression techniques are integrated to reduce communication complexity. CEFGL achieves optimal classification accuracy in various environments across 16 datasets, outperforming the state-of-the-art method FedStar by 5.64% on cross-datasets setting CHEM while reducing communication bits by a factor of 18.58 and time by a factor of 1.65. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers learn from private data without sharing it is called federated graph learning (FGL). FGL helps different devices process their own data privately, but this can be tricky because the data might not be organized in a consistent way. To make things better, researchers created an algorithm that combines the strengths of different models and reduces the amount of information sent between devices. This makes it faster and more accurate than existing methods. |
Keywords
» Artificial intelligence » Classification » Stochastic gradient descent
