On synthetic and Ausgrid energy data, federated LSTM forecasting is less accurate than centralized training for non-linear distributions, and detrending choice matters; differencing helps most in synthetic experiments, while mean or quadratic removal help slightly on real data.
Electrical load forecasting using edge computing and federated learning
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D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
On synthetic and Ausgrid energy data, federated LSTM forecasting is less accurate than centralized training for non-linear distributions, and detrending choice matters; differencing helps most in synthetic experiments, while mean or quadratic removal help slightly on real data.