Summary of Fedgat: a Privacy-preserving Federated Approximation Algorithm For Graph Attention Networks, by Siddharth Ambekar et al.
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
by Siddharth Ambekar, Yuhang Yao, Ryan Li, Carlee Joe-Wong
First submitted to arxiv on: 20 Dec 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 paper proposes a novel federated learning algorithm for graph attention networks (GATs) called Federated Graph Attention Network (FedGAT). FedGAT addresses the challenge of cross-client edges in graph learning, where retaining these edges incurs significant communication overhead or dropping them reduces model performance. The authors introduce an algorithm that approximates the behavior of GATs with provable bounds on the approximation error, requiring only one pre-training communication round and significantly reducing communication overhead for federated GAT training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to train graph attention networks (GATs) in a distributed manner while preserving privacy. This is important because social media sites or online marketplaces often have graphs that are naturally partitioned across clients, making it difficult to share information. The authors propose an algorithm called Federated Graph Attention Network (FedGAT) that can be used for semi-supervised node classification. It’s like a shortcut that allows the model to learn from other nodes without having to share too much information. |
Keywords
» Artificial intelligence » Attention » Classification » Federated learning » Graph attention network » Semi supervised
