Privacy-Preserving AI in Agriculture: A Review of Federated Learning Approaches

Author: Jigar A. Soni, Nitin R. Pandya, Rajan B. Patel, Jinit S. Raval

Published Online: July 1, 2025

DOI: http://doi.org/10.63766/spujstmr.24.000030

Abstract

References

Federated Machine Learning (FML) is a revolutionary approach for training machine learning models while ensuring data privacy and security. This paper provides a comprehensive analysis of FML and its applications in agriculture. We examine how FML enhances predictive analytics, fosters collaborative learning among agricultural stakeholders, and addresses challenges such as communication constraints and data heterogeneity. Additionally, we explore real-world implementations and present relevant datasets that highlight the impact of FML on modern agricultural practices.

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Keywords: Federated Learning, Machine Learning Agriculture, Precision Agriculture, Smart Farming, IoT in Agriculture, Crop Monitoring

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SPU - Journal of Science, Technology and Management Research

An Official Publication of Sankalchand Patel University, Visnagar, Gujarat, India.

Volume - 2 | Issue - 1 (July)

Privacy-Preserving AI in Agriculture: A Review of Federated Learning Approaches