The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.

翻译：计算能力的显著进步为人工智能在医疗保健和医学科学的不同应用领域创造了广阔机遇。一种基于混合联邦学习的集成方法，通过结合SWIN Transformer与CNN进行肺部疾病诊断，融合了人工智能与联邦学习的前沿技术。鉴于医疗专家和医院将共享数据空间，基于这些数据，借助人工智能与联邦学习技术的整合，我们可以构建一个安全分布式的医疗数据处理系统，并建立一个高效可靠的诊断体系。所提出的混合模型能够基于X射线报告检测COVID-19与肺炎。我们将采用Tensorflow和Keras提供的最新先进技术，结合微软开发的Vision Transformer架构，共同应对这场需要全球携手抗击的疫情。本研究重点运用最新的CNN模型（DenseNet201、Inception V3、VGG 19）与Transformer模型SWIN Transformer构建混合模型，旨在为医疗领域的医师提供可靠的辅助诊断方案。本研究将深入探讨基于联邦学习的混合人工智能模型如何通过实时持续学习方法提升疾病诊断与患者病情严重程度预测的准确性，并阐释联邦学习的整合如何保障混合模型的安全性及信息的真实性。