Abstract
The agricultural sector is increasingly leveraging digital technologies to enhance efficiency and sustainability. However, the sensitive nature of farm-specific data, particularly regarding pest infestations, poses significant privacy challenges for collaborative data analysis. This paper investigates the application of federated learning (FL) frameworks for privacy-preserving pest recognition across a multi-farm data ecosystem. Traditional centralized approaches require data aggregation, which is often infeasible due to privacy concerns and data ownership issues. FL offers a decentralized paradigm where models are trained locally on individual farms, and only model updates are shared, thus preserving raw data privacy. We explore various FL architectures and their suitability for pest recognition tasks, considering the inherent heterogeneity of agricultural data and the need for robust privacy guarantees. The study reviews existing FL frameworks, including those enhanced with blockchain for enhanced security and trust [2, 9, 15, 21, 22], and discusses their potential to enable collaborative pest identification without compromising farm-level data confidentiality [1, 4, 6, 7, 10, 11, 12, 13, 14, 16, 18, 19, 20]. Challenges such as data imbalance, communication overhead, and model robustness in diverse farm environments are addressed. This research aims to provide a foundational understanding for developing and deploying effective FL solutions for privacy-aware pest management, contributing to more resilient and data-secure smart farming systems.