Summary of Fedtad: Topology-aware Data-free Knowledge Distillation For Subgraph Federated Learning, by Yinlin Zhu et al.
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
by Yinlin Zhu, Xunkai Li, Zhengyu Wu, Di Wu, Miao Hu, Rong-Hua Li
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Social and Information Networks (cs.SI)
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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 introduces Subgraph Federated Learning (Subgraph-FL), a distributed approach for training Graph Neural Networks (GNNs) by multiple clients with heterogeneous subgraphs. The challenge lies in subgraph heterogeneity, which affects the performance of global GNNs. By decoupling node and topology variations, the study reveals that they impact label distribution and structure homophily, leading to differences in local GNN knowledge reliability. To address this, the authors propose Topology-Aware Data-Free Knowledge Distillation Technology (FedTAD), which enhances reliable knowledge transfer from local to global models. The paper demonstrates FedTAD’s superiority on six public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Subgraph Federated Learning is a new way for computers to work together and train Graph Neural Networks. Right now, this method has a problem because the subgraphs (small parts of the graph) are different from each other. This makes it hard for the global model to learn well. Researchers have been trying to understand why this happens, and they found that two things make the difference: how the nodes are labeled and how the graph is structured. Because of these differences, the local models learn different things, which can be misleading when combining them. To fix this, the authors came up with a new way called Topology-Aware Data-Free Knowledge Distillation Technology. This method helps the global model learn from the local models more reliably. The paper shows that this method works better than other methods on several datasets. |
Keywords
» Artificial intelligence » Federated learning » Gnn » Knowledge distillation
