Federated Machine Learning Framework for Soil Classification in Smart Agriculture

Marwen Ghabi, Sofiane Khalfallah, Hela Ltifi

2025

Abstract

In the area of smart agriculture, data management and analysis play a key role in improving agricultural practices. However, the centralization of data poses major challenges in terms of confidentiality, especially due to the sensitivity of information collected from farms. Federated learning addresses these concerns by enabling the training of AI models in a decentralized manner, where data remains localized while sharing only model updates. This approach ensures confidentiality while facilitating collaboration between different data sources. This study presents an innovative solution that combines federated learning with a modular microservices-based architecture to deploy predictive models as a machine learning service. This architecture, consisting of microservices dedicated to data management, local model formation, federated aggregation, and Application Programming Interface (API) delivery, enables real-time predictions to be delivered in a scalable and resilient manner. To illustrate this approach, a case study on soil type classification was conducted. The results show that our method not only preserves the confidentiality of distributed agricultural data, but also improves the accuracy of agricultural recommendations. The integration of federated learning into a microservices architecture represents a significant step forward, offering new perspectives for artificial intelligence in complex environments requiring confidentiality and scalability.

Paper Citation

in Harvard Style

Ghabi M., Khalfallah S. and Ltifi H. (2025). Federated Machine Learning Framework for Soil Classification in Smart Agriculture. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 782-788. DOI: 10.5220/0013182200003890

in Bibtex Style

@conference{icaart25,
author={Marwen Ghabi and Sofiane Khalfallah and Hela Ltifi},
title={Federated Machine Learning Framework for Soil Classification in Smart Agriculture},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={782-788},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013182200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Federated Machine Learning Framework for Soil Classification in Smart Agriculture
SN - 978-989-758-737-5
AU - Ghabi M.
AU - Khalfallah S.
AU - Ltifi H.
PY - 2025
SP - 782
EP - 788
DO - 10.5220/0013182200003890
PB - SciTePress