Federated Learning Approaches in Precision Agriculture: A Systematic Review of Current Trends, Challenges, and Opportunities

The rapid expansion of digital agriculture driven by sensors, drones, IoT devices, and remote sensing technologies has generated unprecedented volumes of farm-level data, intensifying concerns surrounding privacy, ownership, and secure data sharing. Federated Learning (FL) has emerged as a promising paradigm that enables distributed model training without exposing raw data, making it well suited for precision agriculture. This systematic review examines 90 peer-reviewed studies published between 2022 and 2025 across major academic databases to evaluate how FL is being applied to enhance agricultural intelligence while reducing raw-data sharing and, in fewer studies, using explicit privacy mechanisms. This review distinguishes between (i) baseline FL, where privacy is primarily achieved by keeping raw data local and (ii) FL augmented with explicit privacy-preserving mechanisms such as differential privacy, homomorphic encryption and secure multi-party computation. Besides, it highlights that horizontal and cross-silo FL architectures dominate current research, particularly in crop disease detection, yield prediction, smart irrigation, and IoT-based farm monitoring. A key finding is that most agricultural FL studies still rely on baseline FL without formal privacy guarantees, and the adoption of DP/HE/SMPC remains limited highlighting a major research gap. Although FL often matches centralized deep-learning accuracy, real-world adoption is limited by non-IID data, communication costs, and constrained compute/connectivity in rural settings. This review summarizes FL architectures, agricultural applications, and the limited use of explicit privacy mechanisms, highlighting key gaps and research directions (personalized/hierarchical FL, federated reinforcement learning, explainable and energy-efficient models, and quantum-enhanced FL).