Unlock Data Sharing in Wind Power Forecasting through Privacy-preserving Federated-Learning: Benchmarking of Mixed and Fully Encrypted Frameworks
Résumé
Federated learning is a technology enabling the privacy-preserving sharing of spatio-temporal data. Most works are only evaluated on a small-scale dataset. We analyse the scaling of a previous model developed by us. Showing that the idea of mixed encryption is able to allow efficient scaling and address this issue.
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