A federated LSTM for household load forecasting that chooses each client's learning rate by validation loss is reported to beat federated averaging and LSTM on a single-household dataset.
A deep learning method for short-term residential load forecasting in smart grid,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach
A federated LSTM for household load forecasting that chooses each client's learning rate by validation loss is reported to beat federated averaging and LSTM on a single-household dataset.