Summary of Fedssp: Federated Graph Learning with Spectral Knowledge and Personalized Preference, by Zihan Tan et al.
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
by Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Personalized Federated Graph Learning (pFGL) is a decentralized training approach for Graph Neural Networks (GNNs) that ensures privacy and accommodates diverse requirements. In cross-domain scenarios, pFGL faces challenges due to structural heterogeneity. Previous methods fail to adapt to local domain shifts while sharing non-generic knowledge globally. Our innovative approach leverages the spectral nature of graphs to reflect domain structural shifts. We share generic spectral knowledge and propose a personalized preference module. Combining these strategies, our framework FedSSP (Shares Spectral knowledge while satisfying graph Preferences) demonstrates superiority in cross-dataset and cross-domain experiments. Code is available at https://github.com/OakleyTan/FedSSP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to train computers without sharing personal data. This paper introduces a new way to do this called Personalized Federated Graph Learning (pFGL). It’s like a private messaging system for computer networks. The big challenge is that different networks have different structures, making it hard to share information between them. Our solution uses the underlying patterns in these structures to understand how they’re changing and adapt our approach accordingly. This results in better performance and more accurate predictions. We tested this method on various datasets and showed its superiority. |
