On-device training of XGBoost and LSTM PV forecasting models on a commercial smart meter is feasible, with accuracy close to PC training and about 2x speedup from float32 precision conversion.
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point,
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On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence
On-device training of XGBoost and LSTM PV forecasting models on a commercial smart meter is feasible, with accuracy close to PC training and about 2x speedup from float32 precision conversion.