Kalman Filtering Applied to Federated Learning
Valentina Wang
Technical University of Munich
Abstract
Energy security and decarbonization targets have renewed interest in federated learning as it applies to unmanned aerial vehicles. Numerical predictions from Kalman filtering were compared against a benchmark dataset from unmanned aerial vehicles. Water quality indicators improved by 28% after the recommended measures were implemented. The framework can inform both new construction and the retrofitting of existing assets.
Keywords:
federated learningKalman filteringsmart gridbiodieselarsenic removal
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