Summary of Against Multifaceted Graph Heterogeneity Via Asymmetric Federated Prompt Learning, by Zhuoning Guo et al.
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
by Zhuoning Guo, Ruiqian Han, Hao Liu
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); 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 introduces Federated Graph Prompt Learning (FedGPL), a novel framework that enables efficient knowledge transfer between multiple parties with diverse graph data and tasks. The challenge in Federated Graph Learning (FGL) is to optimize models on heterogeneous data for different tasks, while preserving universal and domain-specific graph knowledge. To address this issue, the authors propose a split federated framework that separates hierarchical knowledge distillation from virtual prompt generation. Two algorithms are developed: Hierarchical Directed Transfer Aggregator (HiDTA) for cross-task knowledge transfer and Virtual Prompt Graph (VPG) for adaptive graph structure generation. Theoretical analyses and extensive experiments demonstrate the superiority of FedGPL over state-of-the-art baselines on large-scale federated graph datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help different groups of people work together to learn from each other’s graphs, even if their data and goals are very different. The problem is that when you try to do this, you have to make sure that everyone’s knowledge is preserved, which can be hard. The authors came up with a solution called Federated Graph Prompt Learning (FedGPL) that splits the work into two parts: one part focuses on preserving overall graph knowledge and another part generates special prompts to help different groups learn from each other. |
Keywords
» Artificial intelligence » Knowledge distillation » Prompt
