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

TL;DR

This paper demonstrates that federated learning enables multiple hospitals to collaboratively train a pneumonia detection model from chest X-rays, achieving high accuracy and privacy preservation without sharing sensitive patient data.

Contribution

It introduces a federated learning approach for pediatric pneumonia detection that handles non-IID data across hospitals, improving performance while maintaining data privacy.

Findings

Achieved 0.900 accuracy and 0.966 ROC-AUC in pneumonia detection

Significant performance gains over single-hospital models (up to 50%)

Validated federated learning's effectiveness in real-world, privacy-sensitive healthcare settings

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.