A Federated Learning Approach for Multi-Crop Rice Yield Prediction

Paper ID: 88

Abstract

This study applies federated learning to predict rice cultivation areas across six districts in Bangladesh, integrating localized models into a global one via the FedAvg algorithm. By improving prediction accuracy and preserving data privacy, it offers a scalable solution for efficient agricultural planning and resource management.

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