A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

A hybrid federated learning (FL) based ensemble approach that integrates the SWIN Transformer and Convolutional Neural Networks (CNNs) has been proposed for diagnosing lung diseases, including pneumonia and COVID-19, from chest X-ray images while preserving data privacy through decentralized training. This approach leverages the self-attention mechanism of transformers to capture complex patterns in medical images and combines it with the feature extraction strength of CNNs such as DenseNet201, InceptionV3, and VGG19, enabling robust disease detection. By utilizing federated learning, the model allows multiple institutions to collaboratively train a shared AI system without exchanging sensitive patient data—each site trains locally and only shares model updates with a central server, ensuring compliance with privacy regulations.

The fusion of SWIN Transformer with CNN architectures within an FL framework supports secure, distributed learning across heterogeneous datasets, which is particularly valuable in healthcare settings where data sharing is restricted. This hybrid model not only improves diagnostic accuracy but also enhances system reliability and generalization by incorporating diverse data sources while maintaining data authenticity and security. Recent studies confirm that combining ensemble methods with federated learning outperforms centralized approaches in both accuracy and privacy preservation, with some models achieving up to 96.63% accuracy on chest X-ray classification tasks. Furthermore, the integration of advanced deep learning techniques like transfer learning and ensemble modeling within FL frameworks has shown significant promise in addressing challenges related to data imbalance, model calibration, and real-world clinical deployment.

This research introduces a novel framework for diagnosing lung diseases that addresses the critical tension between data utility and patient privacy. By leveraging a Hybrid Federated Learning (FL) architecture, the authors propose an ensemble method that integrates the strengths of SWIN Transformers and Convolutional Neural Networks (CNNs). This system allows decentralized medical institutions to collaboratively train a diagnostic model on local data—such as chest X-rays or CT scans—without sharing sensitive patient records. The fusion architecture specifically combines the SWIN Transformer's ability to model long-range global dependencies with the CNN's proficiency in extracting local textural features, providing a comprehensive analysis of medical imagery within a secure, privacy-preserving FL environment.

The key contribution of this work lies in its unique hybrid ensemble strategy applied within the constraints of federated settings. Unlike traditional FL implementations that might rely on a single backbone architecture, this approach aggregates the complementary feature maps of both transformer and convolutional models. The study demonstrates that this fusion improves diagnostic accuracy and robustness, particularly when dealing with heterogeneous, non-IID data typical across different hospitals. By optimizing the aggregation of these distinct model types, the framework mitigates common FL challenges such as communication overhead and convergence instability, resulting in a global model that generalizes effectively across diverse patient populations.

This research is significant because it pushes the boundary of what is possible in privacy-preserving medical AI. As healthcare increasingly relies on large-scale data sharing, strict privacy regulations often impede the development of high-performance models. By successfully deploying complex transformer-based architectures alongside CNNs in a federated context, this work proves that state-of-the-art deep learning techniques can be adapted for secure, distributed environments. This facilitates broader cross-institutional collaboration, ultimately leading to more accurate and accessible lung disease diagnostics without compromising data sovereignty.

Summary of "A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN"

This paper introduces a hybrid federated learning (FL) framework that combines the SWIN Transformer and convolutional neural networks (CNNs) to improve lung disease diagnosis while preserving patient privacy. The approach leverages the strengths of both architectures: the SWIN Transformer’s ability to capture long-range dependencies in medical imaging and the CNN’s efficiency in local feature extraction. By operating in a federated setting, the model aggregates insights from decentralized datasets without centralizing raw patient data, addressing critical privacy and regulatory concerns in healthcare AI.

The key contributions include: 1. Hybrid Model Architecture: A novel ensemble of SWIN Transformer and CNN, optimized for medical image analysis, where the transformer handles global contextual features and the CNN refines local patterns. 2. Federated Learning Adaptation: A privacy-preserving FL protocol that enables collaborative model training across multiple institutions without sharing raw data, improving generalization while mitigating data silos. 3. Diagnostic Performance: Empirical validation on lung disease datasets (e.g., COVID-19, pneumonia) demonstrates competitive accuracy compared to centralized baselines, with robustness to heterogeneous data distributions.

This work is significant for healthcare AI, as it bridges the gap between high-performance deep learning models and real-world deployment constraints (e.g., GDPR, HIPAA). By demonstrating that federated hybrid models can achieve state-of-the-art results without compromising privacy, the paper offers a scalable solution for multi-institutional medical diagnostics. It also highlights the potential of transformer-CNN fusion in medical imaging, paving the way for more interpretable and efficient diagnostic tools.