Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data

2025-11-12

AI Summary

Pediatric pneumonia diagnosis from chest X-rays (CXRs) faces challenges including data silos, heterogeneous non-IID data distributions across institutions, strict privacy compliance requirements, and high costs associated with medical image transmission. Method: This study pioneers the application of federated learning (FL) to multi-center pediatric pneumonia detection. Leveraging the Sherpa.ai FL platform, we establish a decentralized training framework that enables collaborative modeling on non-IID CXR data without sharing raw images. Contribution/Results: The federated model achieves an accuracy of 0.900 and ROC-AUC of 0.966—nearly a 50% improvement over single-center baselines—demonstrating superior generalizability and clinical robustness. This work validates FL’s feasibility for low-data, high-privacy medical imaging tasks, particularly in rare pediatric diseases, and establishes a reproducible technical paradigm for cross-institutional AI collaboration in healthcare.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Early and accurate pneumonia detection from chest X-rays (CXRs) is clinically critical to expedite treatment and isolation, reduce complications, and curb unnecessary antibiotic use. Although artificial intelligence (AI) substantially improves CXR-based detection, development is hindered by globally distributed data, high inter-hospital variability, and strict privacy regulations (e.g., HIPAA, GDPR) that make centralization impractical. These constraints are compounded by heterogeneous imaging protocols, uneven data availability, and the costs of transferring large medical images across geographically dispersed sites. In this paper, we evaluate Federated Learning (FL) using the Sherpa.ai FL platform, enabling multiple hospitals (nodes) to collaboratively train a CXR classifier for pneumonia while keeping data in place and private. Using the Pediatric Pneumonia Chest X-ray dataset, we simulate cross-hospital collaboration with non-independent and non-identically distributed (non-IID) data, reproducing real-world variability across institutions and jurisdictions. Our experiments demonstrate that collaborative and privacy-preserving training across multiple hospitals via FL led to a dramatic performance improvement achieving 0.900 Accuracy and 0.966 ROC-AUC, corresponding to 47.5% and 50.0% gains over single-hospital models (0.610; 0.644), without transferring any patient CXR. These results indicate that FL delivers high-performing, generalizable, secure and private pneumonia detection across healthcare networks, with data kept local. This is especially relevant for rare diseases, where FL enables secure multi-institutional collaboration without data movement, representing a breakthrough for accelerating diagnosis and treatment development in low-data domains.

Problem

• Enabling collaborative pneumonia detection without sharing patient data across hospitals
• Addressing data privacy regulations that prevent centralized medical image analysis
• Overcoming distributed data challenges and variability in pediatric pneumonia diagnosis

Innovation

• Federated Learning enables collaborative training without data sharing
• Sherpa.ai platform trains CXR classifier while keeping data private
• Multiple hospitals improve pneumonia detection with local data processing