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This paper presents a federated learning model for greenhouse temperature prediction based on the N-BEATS deep learning model. Precise greenhouse temperature forecasting facilitates the improvement of greenhouse environmental control and thereby reduces energy consumption as well as maximizes agricultural yield. In addition to addressing the prediction issue, the model addresses problems concerning data privacy and decentralized learning models in general for smart agriculture. The test's findings show that the federated learning model with the N-BEATS structure achieves full-size predictive power with Mean squared error (MSE) of 0.0053 and Root mean square error (RMSE) of 0.0975, and with stable convergence. Empirical proof validates the use of the federated learning model in the greenhouse climate regulation as proof of strong predictive capacity as well as the maintenance of data confidentiality. There's a lot of promises with the current model for actual use in smart agriculture, as well as with comprehensive machine learning models running in distributed environments.

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Federated Learning for Greenhouse Temperature Prediction: A Privacy-Preserving Approach Using N-BEATS

  • 会议论文

  • 发表日期：

    2025年

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  • Trang Ha,
  • Phuong Anh Nguyen,
  • Tung Vu,
  • Ngoc Le

摘要

This paper presents a federated learning model for greenhouse temperature prediction based on the N-BEATS deep learning model. Precise greenhouse temperature forecasting facilitates the improvement of greenhouse environmental control and thereby reduces energy consumption as well as maximizes agricultural yield. In addition to addressing the prediction issue, the model addresses problems concerning data privacy and decentralized learning models in general for smart agriculture. The test's findings show that the federated learning model with the N-BEATS structure achieves full-size predictive power with Mean squared error (MSE) of 0.0053 and Root mean square error (RMSE) of 0.0975, and with stable convergence. Empirical proof validates the use of the federated learning model in the greenhouse climate regulation as proof of strong predictive capacity as well as the maintenance of data confidentiality. There's a lot of promises with the current model for actual use in smart agriculture, as well as with comprehensive machine learning models running in distributed environments.