Federated Learning Framework for Privacy-Preserving Kidney Stone Detection

Najiyya Younas; Omar Abdulkader; Yaser Ali Shah; Muhammad Jawad Ikram; Jebran Khan; Amaad Khalil

cs.CVSep 17, 2026

Federated Learning Framework for Privacy-Preserving Kidney Stone Detection

Authors: Najiyya Younas, Omar Abdulkader, Yaser Ali Shah, Muhammad Jawad Ikram, Jebran Khan, Amaad Khalil

Organizations: Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan · Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia · Department of Artificial Intelligence, Kyungdong University Global, Toseong-myeon 24764, South Korea. · Department of Computer Systems Engineering, University of Engineering & Teschnology,Peshawar, 25000, Pakistan.

Abstract

Recent innovations in deep learning have significantly enhanced the diagnosis of medical images, although they are based on the use of centralized data storage that pose severe threats to patient privacy and medical data security. To address this issue, this research proposes a Federated Learning (FL) model that is coupled with an optimized YOLOv8 network to detect the kidney stones on a computed tomography (CT) image and at the same time, protect privacy of the patients. The suggested system can help various medical organizations to jointly train a common model without exchanging the information about the patients. This is to ensure that data protection laws like GDPR and HIPAA are adhered to. The residual feature fusion and DropBlock regularization among other architectural improvements are also included in YOLOv8 to enhance detection robustness and minimize overfitting. Experimental analysis carried out on a distributed CT dataset demonstrated that the federated YOLOv8 model has a mAP at 50 of 0.733 and is able to keep the data confidential. Moreover, its lean design facilitates fast edge deployment and real-time inference across a clinical setting. Altogether, these findings indicate that Federated Learning is a safe and efficient solution to AI-assisted diagnosis in contemporary healthcare when combined with the use of sophisticated object detection models.

Explore similar work

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
Jul 2, 2026Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3Federated Learning Multi-Drone Coordination
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
May 7, 2026Mianyong Ding, Maximilian Knoll, Semi Harrabi +7Federated Learning Lesion Segmentation
Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning
Jul 28, 2026Md Zahid Hasan Ontor, Md Al Amin, Anik Dev Nath +1Chronic Kidney Disease Clinical Prediction

Jul 2, 2026 · cs.LGJ/K move · Enter open · S save

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris, Joaquin del Rio+2

Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environments, infrastructure monitoring and defense applications. Robust model performance in such environments depends on large, continuously updated datasets. However, training high-performing detectors typically requires centralizing aerial imagery, which raises privacy, regulatory, storage, and bandwidth challenges. This is especially problematic in distributed drone deployments, where visual data is generated onboard and is often impractical or undesirable to transfer to a centralized infrastructure. In this work, we apply Federated Learning (FL) for object detection, enabling drones to improve a shared model while keeping image data local and private. We implement a federated object detection pipeline using the Sherpa.ai FL platform on the KIIT-MiTA dataset, and compare it with Single-drone and Centralized baselines using mean Average Precision (mAP) at IoU thresholds of 0.50 and 0.50-0.95. In our experiments, the proposed FL approach remains close to Centralized training while dramatically improving over Single-drone training, with the best lightweight model (YOLO26 nano), suitable for deployment even on very limited edge infrastructure, achieving relative gains of 52.89% and 67.80% in mAP@0.50 and mAP@0.50:0.95, respectively. These results show that FL enables scalable, high-performing, and privacy-preserving object detection across distributed drone fleets without data centralization.

Federated Learning Multi-Drone Coordination Object Detection Multi-Sensor Fusion

Open paper Ask about it PDF

Abstract

Explore similar work

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning