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.
Gupta et al. (Wed,) studied this question.
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