Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy

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ABSTRACT Early diagnosis of thoracic diseases using chest x‐ray imaging remains a critical challenge, particularly in resource‐constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized solution by enabling collaborative model training without sharing sensitive patient data. However, integrating privacy‐preserving mechanisms such as differential privacy (DP) introduces additional challenges related to performance degradation and computational overhead. In this study, we present a unified FL framework for multi‐label thoracic disease classification using multiple convolutional neural network (CNN) architectures, including ResNet50, DenseNet169, EfficientNet variants and MobileNetV3. Unlike prior studies focusing on single‐model evaluation, this work provides a controlled comparative analysis under identical FL settings and investigates the impact of client scalability (5–10 clients) on model performance. Furthermore, we conduct a comprehensive empirical analysis of the privacy utility trade‐off by integrating DP with varying privacy budgets ( ε = 1, 15 and 30). Experimental results on the CheXpert and NIH Chest x‐ray14 datasets demonstrate that the proposed EfficientNet‐B3‐based federated model achieves a mean AUC of 0.8027, while maintaining robustness across decentralized settings. The integration of DP leads to a predictable reduction in performance, with mean AUC ranging from 0.60 to 0.64, highlighting the inherent trade‐off between privacy and diagnostic accuracy. The findings emphasize the practical viability of FL for privacy‐sensitive medical imaging applications and provide insights into model selection, scalability and privacy configuration for real‐world deployment. The source code for this study is publicly accessible at https://github.com/Zulqarnain8‐8/FEDERATED_LEARNING_FOR_THORACIC_DISEASE_CLASSIFICATION .

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