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Found 2 papers, 1 papers with code
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
In particular, by conducting graph sampling beforehand, DiskGNN acquires the node features that will be accessed by model computation, and such information is utilized to pack the target node features contiguously on disk to avoid read amplification.
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy and gradient protection.
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