Summary of Hybrid Fedgraph: An Efficient Hybrid Federated Learning Algorithm Using Graph Convolutional Neural Network, by Jaeyeon Jang et al.
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
by Jaeyeon Jang, Diego Klabjan, Veena Mendiratta, Fanfei Meng
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper proposes a generalized algorithm called FedGraph for federated learning in hybrid scenarios, where clients share both feature and sample information. The algorithm utilizes graph convolutional neural networks to capture feature-sharing patterns and deep neural networks to learn features from subsets of clients. A clustering algorithm is also developed to aggregate features while preserving data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for machines to learn together without sharing their individual data. It creates an algorithm that uses special kinds of artificial intelligence called graph convolutional neural networks to find patterns in the shared information between devices. This helps the machines learn from each other better. The research also includes a simple method to combine the learned information while keeping it private. |
Keywords
» Artificial intelligence » Clustering » Federated learning
