A Distributed Privacy Preserved Federated Learning Approach for Revolutionizing Pneumonia Detection in Isolated Heterogenous Data Silos

Pneumonia is a respiratory lung contamination that ranges in severity from mild to lethal outcomes. The analysis of tomographic images is the most significant method of pneumonia detection. The image analysis requires expertise and proficiency to diagnose the disease correctly. The medical reports with multiple diseases have overlapping symptoms, which may lead to misdiagnosis and deferred identification. The misdiagnosis results in increased healthcare costs, worsened medical conditions, and legal implications. Centralized deep learning enhances the feature extraction process and optimally improves the prediction outcomes; however, these models have data privacy concerns due to centralized storage systems. The healthcare departments follow the Health Insurance Portability and Accountability Act. (HIPAA) to maintain the retaining of patient data and improve the portability and continuity of health insurance coverage. In the proposed work, federated learning has been utilized to enhance data privacy and deal with imbalanced and diverse data silos. This distributed privacy-preserved model has been employed with a pooled dataset curated from multiple sources in a 5-client architecture. The model was implemented with the FedAVG aggregation technique in independent and identically distributed (IID) and non-IID data distributions. The outcomes of the model exhibit 87.62% accuracy with IID and 86.15% accuracy with non-IID distributions. The comparison of these outcomes with the existing studies shows that the proposed model outperforms by exhibiting better performance and resulting in the minimum loss of 0.4041 and 0.4139 with IID and non-IID distributions, respectively.