Summary of Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy, by Linfeng Luo et al.
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy
by Linfeng Luo, Fengxiao Tang, Xiyu Liu, Zhiqi Guo, Zihao Qiu, Ming Zhao
First submitted to arxiv on: 9 Aug 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 The paper introduces FedHGL, a novel federated hypergraph learning algorithm for collaborative training of Graph Neural Networks (GNNs) across clients without sharing raw node features. This approach enables data mining on subgraphs within distributed systems, particularly effective for high-order relationships between nodes in hypergraphs. The method involves pre-propagation hyperedge completion and local differential privacy (LDP) mechanisms to ensure the preservation of original node features during feature aggregation. Experimental results demonstrate the performance advantages of FedHGL over traditional federated graph learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedHGL is a new way for computers to work together on big data projects that involve complicated connections between things. Right now, it’s hard to do this kind of project when the data is spread out across many different places. The new approach helps solve this problem by allowing computers to work together without sharing all the details about each piece of information. |
