Secure and Decentralized Plant Disease Detection via Federated Learning with Differential Privacy and Homomorphic Encryption

Vetripriya M.¹, S. Amsavalli², R. Sivasankari¹, Vetri Selvan M.³, N. Kanimozhi⁴

¹Dept. of Computer Science and Engineering, B.S.Abdur Rahman Crescent Institute of Science and Technology

²Dept. of Computer Application, B.S.Abdur Rahman Crescent Institute of Science and Technology

³Dept. of Artificial Intelligence and Data Science, Panimalar Engineering College, Tamil Nadu, India

⁴Dept of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur. Chennai.

Emails: vetripriya@crescent.education; amsavalli@crescent.education; sivasankari.rp@crescent.education; vetrinelson7@gmail.com; kanimozn@srmist.edu.in

Abstract

Plant disease detection using deep learning has achieved high accuracy, but traditional centralized training poses significant privacy risks and incurs high data transmission costs. This study presents a privacy-preserving federated learning (FL) framework for plant disease diagnosis that enables decentralized model training across geographically distributed agricultural sites. Rather than transferring raw farm data to a central server, local models are trained on edge devices and share only model updates. To address data heterogeneity from diverse climates, soils, and plant species, we introduce adaptive aggregation strategies that improve model generalization. Furthermore, we incorporate differential privacy and homomorphic encryption to ensure secure model updates and protect sensitive information from potential breaches. Experimental evaluations on benchmark datasets, including Plant Village and real-world field images, show that the proposed FL-based system achieves comparable accuracy to centralized models while significantly enhancing data privacy and reducing communication overhead. The framework maintains over 93% classification accuracy across 38 plant disease categories, with minimal degradation from added privacy mechanisms. Additionally, we analyze the trade-off between accuracy and communication efficiency, demonstrating the method’s practicality in bandwidth-constrained rural environments. The proposed system offers a scalable, secure, and field-deployable solution for real-time plant disease monitoring, supporting the widespread adoption of AI in precision agriculture without compromising data confidentiality.

Keywords: Federated learning; Plant disease detection; Privacy-preserving AI; Decentralized deep learning; Differential privacy; Precision agriculture