Summary of Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-densely Connected Gnns, by Longwen Wang and Jianchun Liu and Zhi Liu and Jinyang Huang
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
by Longwen Wang, Jianchun Liu, Zhi Liu, Jinyang Huang
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated Graph Learning (FGL) is an emerging technology enabling clients to train powerful Graph Neural Networks (GNNs) without exposing private data. However, FGL faces the challenge of non-Independent and Identically Distributed (non-IID) graphs with diverse node and edge structures across domains. Existing methods overlook structural knowledge or capture it at increased resource costs, detrimental to distributed paradigms. We propose FedDense, a novel FGL framework optimizing inherent structural knowledge utilization efficiency. It encodes structural knowledge within graph data alongside node features and introduces a Dual-Densely Connected (DDC) GNN architecture exploiting multi-scale feature insights. To address resource limitations, we devise narrow layers and adopt selective parameter sharing to reduce costs substantially. We conduct extensive experiments across 4 domains using 15 datasets, demonstrating FedDense consistently outperforms baselines while demanding minimal resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Graph Learning lets computers train powerful networks without sharing private data. However, this technology faces a big challenge: the graphs used are very different from each other and contain various structures. Existing methods either don’t use these structural differences or do so at the cost of using more resources, which is not good for distributed training. To solve this problem, we created FedDense, a new framework that makes efficient use of the inherent structure in graph data. It does this by encoding the structural knowledge within the graph data itself and introducing a special type of neural network architecture that uses information from different scales. We also designed our framework to work efficiently with limited resources. Our experiments on 15 datasets across 4 domains show that FedDense outperforms existing methods while using minimal resources. |
Keywords
» Artificial intelligence » Gnn » Neural network
