Federated Learning in Smart Agriculture: Applications, Challenges, and Solutions

Abstract

Federated learning (FL) offers a promising approach to enhancing smart agriculture by leveraging decentralized data to improve model accuracy and efficiency. This paper explores the integration of FL into various agricultural applications, including precision farming, pest and disease detection, crop yield prediction, soil health monitoring, and climate impact analysis. FL enables the aggregation of data from multiple farms without centralized data storage, preserving privacy while optimizing resource allocation and decisions. The paper addresses key challenges associated with implementing FL in agriculture, such as data heterogeneity, privacy and security concerns, infrastructure limitations, and communication overhead. Strategies to overcome these challenges are discussed, including advanced model algorithms, privacy-preserving techniques, and infrastructure improvements. Future directions emphasize the need for enhanced model robustness, interdisciplinary collaboration, and real-world validation to expand FL applications and ensure practical deployment. The insights provided aim to guide researchers and practitioners in leveraging FL to advance agricultural productivity and sustainability. © 2026 Scrivener Publishing LLC.