MODEL ARCHITECTURE AND PERSPECTIVES OF FEDERATED LEARNING FOR SUSTAINABLE AGRICULTURE IN BULGARIA: MODEL ARCHITECTURE AND PERSPECTIVES OF FEDERATED LEARNING FOR SUSTAINABLE AGRICULTURE IN BULGARIA - Ivanova, Desislava; Ivanova, Iliyana (JOURNAL SCIENTIFIC AND APPLIED RESEARCH, 2025)
This paper presents a review of the application of Federated Learning (FL) in sustainable agriculture, with a specific focus on its potential integration within the Bulgarian agricultural sector. FL is a decentralized machine learning technology that enables multiple data sources to collaboratively train models without sharing raw data, thus preserving privacy and improving data security. The approach supports optimization of irrigation, fertilization, and pest control through locally adapted models and predictive analytics. The study analyzes the architecture, advantages, and limitations of FL, reviewing international case studies and exploring its relevance to Bulgarian conditions, such as regional climate variability and diverse soil types. Emphasis is placed on the potential role of FL in enhancing decision-making, reducing environmental impact, and supporting digital transformation in agriculture. Recommendations for implementation in Bulgarian farms and research institutions are also provided.