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arXiv Computer Vision By Kriti Saini, Manikandan P

FedXCrop: Privacy-Preserving Crop Disease Detection via Federated Learning with Post-Aggregation Explainability under Non-IID Farm Distributions

Read the original on arXiv Computer Vision →

FedXCrop is a federated learning pipeline for crop disease classification that preserves farmer privacy while providing post‑aggregation explainability. The system uses a MobileNetV2 model and achieves near‑centralized accuracy (98.81%–99.38%) across five clients, even under non‑IID label skew. Grad‑CAM and SmoothGrad explanations are quantitatively evaluated, showing that federated models largely retain explainability characteristics of centralized models, though agreement decreases with increased heterogeneity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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