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

A Hybrid Federated Learning-Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN proposes a secure and distributed system for medical data processing by integrating federated learning with a hybrid AI model composed of SWIN Transformer and CNN architectures. This approach enables the detection of pneumonia and COVID-19 from chest X-ray images while preserving data privacy, as it eliminates the need to share sensitive patient data across institutions. The model leverages advanced deep learning frameworks such as TensorFlow and Keras, incorporating state-of-the-art CNNs like DenseNet201, Inception V3, and VGG19 alongside the SWIN Transformer to enhance diagnostic accuracy and robustness. By utilizing a federated learning framework, the system supports continual learning across decentralized healthcare environments, improving disease diagnosis and severity prediction without compromising data security or authenticity. This method aligns with broader efforts in privacy-preserving AI for healthcare, demonstrating potential for scalable and ethical deployment in real-world clinical settings.

This research addresses the dual challenges of diagnostic accuracy and data privacy in medical imaging by proposing a hybrid federated learning framework for lung disease diagnosis. The core innovation lies in the architectural fusion of a SWIN Transformer, which excels at modeling long-range global dependencies through shifted windowed self-attention, with a Convolutional Neural Network (CNN), renowned for extracting fine-grained local spatial features. By training this ensemble model in a federated setting, the approach allows distributed medical institutions to collaboratively learn a shared diagnostic model without exchanging raw patient data, thereby adhering to strict data privacy regulations and overcoming the limitations of isolated, siloed datasets.

The study demonstrates that the synergistic combination of these two distinct architectures within an ensemble yields superior performance compared to standalone models. The fusion mechanism effectively captures both the local texture nuances of lung pathologies and the broader contextual relationships within the image, leading to robust feature representations. Furthermore, the federated learning strategy ensures that the global model benefits from diverse, decentralized data sources, enhancing its generalizability across different demographic populations and imaging equipment variations.

This work is significant as it bridges the gap between state-of-the-art deep learning capabilities and practical, privacy-preserving healthcare deployment. By integrating the computational efficiency and feature extraction strengths of CNNs with the representational power of Transformers, the research offers a scalable solution for improving computer-aided diagnosis systems. It highlights a viable path forward for medical AI research, where high accuracy is achievable without compromising patient confidentiality, encouraging broader cross-institutional collaboration in the fight against respiratory diseases.

Summary: Hybrid Federated Learning for Lung Disease Diagnosis

This paper introduces a hybrid federated learning (FL) ensemble model combining the Swin Transformer and CNN architectures to improve lung disease diagnosis while preserving patient data privacy. The approach leverages federated learning to train models across multiple medical institutions without centralizing raw imaging data, addressing privacy concerns in healthcare. The Swin Transformer captures long-range dependencies in chest X-rays, while the CNN extracts local features, forming a robust ensemble for classification. A weighted voting mechanism then aggregates predictions from both models, enhancing diagnostic accuracy.

The key contributions include: - A privacy-preserving FL framework that mitigates data silos in healthcare while improving model generalization. - An efficient fusion strategy between transformers and CNNs, optimizing feature extraction for lung pathology detection. - Empirical validation showing superior performance over standalone models or traditional FL approaches.

This work is significant because it addresses critical challenges in medical AI deployment—namely, data privacy, model interpretability, and scalability—while demonstrating state-of-the-art results in lung disease diagnosis. The hybrid FL ensemble approach could serve as a blueprint for deploying AI in decentralized healthcare settings.

Source: [arXiv:2602.17566](https://arxiv.org/abs/2602.17566)