arXiv Machine Learning September 18, 2026 By Najiyya Younas, Omar Abdulkader, Yaser Ali Shah, Muhammad Jawad Ikram, Jebran Khan, Amaad Khalil

Federated Learning Framework for Privacy-Preserving Kidney Stone Detection

Read the original on arXiv Machine Learning →

The paper proposes a Federated Learning framework that integrates an optimized YOLOv8 network for detecting kidney stones in CT images while preserving patient privacy. By enabling multiple medical institutions to collaboratively train a shared model without exchanging patient data, the approach complies with GDPR and HIPAA regulations. Experiments on a distributed CT dataset show a 0.733 mAP@50 and demonstrate fast, real‑time inference suitable for clinical deployment.

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 Machine Learning.

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