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Precision agriculture depends on distributed data from sensors, remote sensing, machinery, crop and livestock monitoring, weather services, and farm-management systems. Collaborative model development can use these resources, yet central collection may intensify privacy, ownership, access, control, cybersecurity, participation, and secondary-use concerns. This article proposes a conceptual federated learning architecture in which farms retain raw data locally while contributing model updates to coordinated cross-farm learning. The architecture integrates use-case definition, stakeholder and governance mapping, farm-data sensitivity assessment, provenance and quality review, local node configuration, local model training, protected update exchange, secure aggregation, global and local evaluation, cross-farm validation, heterogeneity assessment, privacy safeguards, security controls, drift monitoring, auditability, and farmer feedback. It distinguishes horizontal federated learning from conditional vertical and transfer configurations and connects these principles to bounded agricultural use cases, including crop monitoring, disease detection, yield prediction, remote-sensing interpretation, livestock monitoring, and human-reviewed decision support. Secure aggregation, differential privacy, homomorphic encryption, and trusted execution environments are treated as context-dependent safeguards rather than guarantees. The contribution links decentralized model development with governance, accountability, communication constraints, non-identically distributed data, validation, monitoring, and claim boundaries. It supports research and design but does not establish certification, legal compliance, superiority, farmer approval, operational safety, or deployment readiness.