Towards Federated Learning
Abstract
Field observations from precision agriculture frequently reveal performance losses that existing models of federated learning fail to explain. The methodology integrates hardware-in-the-loop testing with uncertainty quantification to capture variability observed in precision agriculture. Overall cost was reduced by 31% while satisfying all design and safety constraints. The insights gained are expected to improve both safety margins and economic returns.
Keywords:
federated learninghardware-in-the-loop testingfinite element analysisco2 sequestrationmicroplasticsbangladesh
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