FedPerKD privacy aware personalized federated learning with knowledge distillation for edge oriented tomato leaf disease detection

Abstract

Tomato leaf diseases threaten global food security by reducing crop yield and quality. Centralized deep-learning detectors require farms to share raw images, which raises privacy, bandwidth, and data-sovereignty concerns. We present FedPer-KD, a privacy-aware federated framework that combines personalization with client-side knowledge distillation: each client keeps its raw images and personalized classifier head locally while the shared MobileNet-V2 feature extractor is aggregated, and a local EfficientNet-B0 teacher supervises the student through softened logits. We evaluate on the PlantVillage tomato leaf dataset (18,345 images, ten classes) using simulated federated clients ( N  = 2, 4, 6) trained for 10, 20, and 30 rounds with five random seeds. FedPer-KD reaches 99.34% test accuracy and 99.33% macro-F1 at six clients and 20 rounds while transmitting only the shared base (~ 8.9 MB per client per round, a 36% saving over FedAvg on the same student). Across 2–6 clients it consistently outperforms FedAvg, FedProx, Scaffold, FedPer, FedAvg + KD, and a capacity-matched FedAvg baseline; paired statistical comparisons with Holm-Bonferroni correction confirm significance ( p  < 0.05) and Cohen’s d > 1.0 on every comparison. We also report ablations over the Dirichlet heterogeneity parameter (alpha = 0.5, 0.3, 0.1), the teacher fine-tuning schedule, the distillation temperature tau and weight alpha, and a battery of synthetic image corruptions. The framework is privacy-aware rather than formally privacy-preserving, is effective under mild client heterogeneity, and is edge-oriented for downstream deployment.