Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy
Muhammad Zulqarnain1, Syed Jawad Hussain1, Muhammad Zeeshan Aslam1
1Department of Computer Science Sir Syed CASE Institute of Technology Islamabad Pakistan.
Healthcare Technology Letters
|May 4, 2026
Summary
Federated learning (FL) enables collaborative training of AI models for diagnosing thoracic diseases from chest X-rays without sharing patient data. Integrating differential privacy (DP) impacts accuracy, showing a trade-off between privacy and diagnostic performance.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Health
Background:
- Early diagnosis of thoracic diseases via chest X-rays is crucial but hindered by privacy concerns and data sharing restrictions in resource-limited settings.
- Federated learning (FL) offers a decentralized approach for collaborative model training without compromising patient data privacy.
- Integrating differential privacy (DP) into FL frameworks can degrade model performance and increase computational load.
Purpose of the Study:
- To develop and evaluate a unified FL framework for multi-label thoracic disease classification using diverse CNN architectures.
- To comparatively analyze the performance of different CNN models (ResNet50, DenseNet169, EfficientNet, MobileNetV3) within identical FL settings.
- To investigate the impact of client scalability and DP integration on diagnostic accuracy and the privacy-utility trade-off.
Main Methods:
- Implemented a federated learning framework for thoracic disease classification on CheXpert and NIH Chest x-ray14 datasets.
- Evaluated multiple CNN architectures including EfficientNet-B3, ResNet50, DenseNet169, and MobileNetV3 under varying client numbers (5-10 clients).
- Integrated differential privacy (DP) with different privacy budgets (ε = 1, 15, 30) to assess its effect on model performance.
Main Results:
- The EfficientNet-B3 based federated model achieved a mean AUC of 0.8027, demonstrating robustness in decentralized environments.
- Client scalability from 5 to 10 clients showed minimal impact on model performance.
- DP integration resulted in a performance decrease, with mean AUC ranging from 0.60 to 0.64, confirming the privacy-utility trade-off.
Conclusions:
- Federated learning is a viable approach for privacy-sensitive medical imaging analysis, particularly for thoracic disease classification.
- Model selection (e.g., EfficientNet-B3), scalability, and DP configuration are critical factors for real-world deployment.
- The study provides empirical evidence on the performance implications of integrating DP in FL for medical diagnostics.
