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In this study, we investigate using federated learning for the CNN model-based prediction of wheat disease severity levels. We employed safe aggregation approaches to training the CNN model on dispersed data while maintaining the privacy and security of the data. The dataset consisted of 8643 photos of wheat plants with 10 severity levels of illness. We assessed the efficacy of the federated learning CNN model using a variety of assessment measures, such as accuracy, precision, recall, F1 score, and AUC ROC, using a validation set. With an accuracy of 0.92, a precision of 0.87, a recall of 0.92, an F1 score of 0.89, and an AUC ROC of 0.95, to findings demonstrated that the federated learning CNN model performed well across all assessment criteria. The effectiveness of the federated learning CNN model and a centralized CNN model trained on the same dataset were also compared. To findings …