PCE-FL: A Personalized, Clustered, and Communication-Efficient Federated Learning Framework for Robust Tomato Leaf Disease Detection

Tomato leaf diseases represent a persistent threat to global food security, causing annual crop losses of 20% to 40%. Although deep learning models achieve accuracies exceeding 95% in centralized settings, their deployment across distributed farms is constrained by data privacy concerns, communication bottlenecks, and heterogeneous data quality. This paper proposes Personalized, Clustered, and Communication-Efficient Federated Learning (PCE-FL), a framework that integrates three synergistic components: (1) server-side client clustering to group farms with similar data distributions for personalized model training; (2) federated knowledge distillation to reduce communication overhead by over 91%; and (3) reputation-based aggregation to ensure robustness against unreliable contributions. Extensive experiments on realistic non-IID simulations of the PlantVillage tomato dataset Dirichlet(α∈{1.0,0.5,0.1}) demonstrate that PCE-FL achieves 89.1% accuracy under extreme heterogeneity (α=0.1), surpassing FedAvg by 10.9 and IFCA by 4.8 percentage points, while maintaining a 91% reduction in communication cost. All improvements are statistically significant (p<0.001). These results advance the practical deployment of privacy-preserving collaborative AI in resource-constrained agricultural environments.