A Comprehensive Overview of Federated Learning for NextGeneration Smart Agriculture: Current Trends, Challenges, and Future Directions
Belghachi Mohammed
Abstract
Federated Learning (FL) is an emerging technique that offers significant potential to enhance smart agriculture by enabling collaborative model training across distributed data sources while preserving data privacy. This paper provides a comprehensive overview of the integration of FL within smart agriculture, emphasizing its role in addressing key challenges, such as data privacy, security, scalability, and data heterogeneity. The paper distinguishes itself from existing reviews by systematically analyzing FL applications in specific agricultural domains, including crop monitoring, soil health management, and livestock management. In addition, it introduces new classifications of FL use cases, focusing on privacy-preserving techniques, scalability issues, and the non-IID nature of agricultural data. Case studies from real-world implementations are used to highlight practical applications and challenges. The paper also discusses recent advances, such as the integration of FL with edge computing and the adoption of personalized federated learning. By presenting a detailed analysis of trends, challenges, and future research directions, this overview fills gaps in existing literature and provides insights into how FL can be leveraged to improve precision, productivity, and sustainability in smart agriculture. Ultimately, the findings underscore the transformative potential of FL to revolutionize data-driven agricultural decision-making and contribute to the development of resilient, privacy-conscious agricultural systems.