In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top level. Within each crop cluster, clients collaboratively train specialized models tailored to specific crop types, which are then aggregated to produce a higher-level global model that integrates knowledge across multiple crops. This hierarchical design enables both local specialization for individual crop types and global generalization across diverse agricultural contexts while preserving data privacy and reducing communication overhead. Experiments demonstrate the effectiveness of the proposed system, showing that local and crop-layer models closely follow actual yield patterns with consistent alignment, significantly outperforming standard machine learning models. The results validate the advantages of hierarchical federated learning in the agricultural context, particularly for scenarios involving heterogeneous farming environments and privacy-sensitive agricultural data.

翻译：本文提出了一种专为智能农业生产系统和作物产量预测设计的新型分层联邦学习架构。我们的方法引入了季节性订阅机制，农场在每个农业季节开始时加入特定作物的集群。所提出的三层架构包括客户层的个体智能农场、中间层的作物特定聚合器以及顶层的全局模型聚合器。在每个作物集群内，客户端协作训练针对特定作物类型的专用模型，这些模型随后被聚合以生成更高层级的全局模型，从而整合跨多种作物的知识。这种分层设计既实现了针对个体作物类型的本地专业化，又能在不同农业环境中实现全局泛化，同时保护数据隐私并减少通信开销。实验证明了所提出系统的有效性，表明本地和作物层模型能够紧密跟踪实际产量模式并保持高度一致性，显著优于标准机器学习模型。结果验证了分层联邦学习在农业环境中的优势，特别是在涉及异构耕作环境和隐私敏感农业数据的场景中。