Summary of Federated Contrastive Learning Of Graph-level Representations, by Xiang Li et al.
Federated Contrastive Learning of Graph-Level Representations
by Xiang Li, Gagan Agrawal, Rajiv Ramnath, Ruoming Jin
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 addresses the need for graph-level representations and clustering/classification techniques in various applications such as malicious network traffic identification and protein property prediction. To overcome data isolation issues due to privacy concerns, lack of trust, regulations, or large data sizes, federated learning for graph-level representations is proposed. The focus is on unsupervised settings, which has not been extensively explored yet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle the problem of creating graph-level representations and clustering/classification techniques for various applications like identifying malicious network traffic and predicting protein properties. To solve data isolation issues due to privacy concerns or large data sizes, they propose using federated learning for graph-level representations in an unsupervised setting. |
Keywords
» Artificial intelligence » Classification » Clustering » Federated learning » Unsupervised
