Agricultural data privacy and federated learning: A review of challenges and opportunities

The rapid digitalization of agriculture has resulted in an unprecedented surge in data collection, necessitating this way the privacy protection in innovative data analytics solutions. Federated Learning emerges as a promising solution since it allows for collaborative model training across decentralized data sources without sharing raw data. This review explores the use of Federated Learning in agriculture, focusing on privacy-preserving methods. We thoroughly reviewed a large corpus of relevant research, examining several Federated Learning types and their application to agricultural scenarios, such as pest and disease detection, crop yield prediction, and resource management. Our findings underscore Federated Learning's potential to revolutionize privacy-preserving data analysis in agriculture by enabling better decision-making through aggregated insights from various farms, while retaining data confidentiality. At the same time, a number of technical complications arise, including data heterogeneity, communication impediments, and limited computational capabilities in rural areas. Data ownership, fairness, and stakeholder trust are significant barriers to widespread use in practice. The present study provides research gaps that need to be addressed to fully use the potential of Federated Learning in agriculture. Tailoring the design of Federated Learning algorithms and adhering to the nature of agricultural data and its peculiarities can promote the enhancement of agriculture-friendly frameworks to ensure privacypreserving mechanisms for agriculture-oriented applications, and the development of frameworks that bear ethical issues in mind and facilitate farmers-based equitable benefit distribution. Since Federated Learning can potentially change the landscape of data-driven agriculture by allowing collaborative data analytics without compromising privacy, it is highly important to overcome the technological and ethical barriers demonstrated in this study, maximizing its impact on sustainable farming practices and innovations.