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STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
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We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph prediction tasks.
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Cited by 3 Pith papers
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FedHERO shares a learned latent-graph generator across federated clients and keeps a private local channel, improving node classification when client graphs have different heterophily patterns.
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MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning
A meta-learning sleep-stage classifier using spatial-temporal hypergraphs improves few-shot cross-subject accuracy on ISRUC and UCD by about 2.5 to 3 percentage points over the strongest baseline.
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