FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN

Digital technologies present unrivaled opportunities to improve healthcare services worldwide. Medical devices and hospitals are now using innovative techniques to diagnose cancer patients. Despite the vast amount of data generated, stored, and communicated to the cloud and edge devices, patient data privacy remains a crucial concern. Federated learning (FL) is a revolutionary distributed learning method with significant potential in medical data processing. However, data privacy, data quality, and model performance issues can make it challenging to develop a resilient model. As a solution to the above-mentioned challenges, a secure and collaborative FedCSCD-GAN framework is proposed for clinical cancer diagnosis under the federated learning and GAN optimization principle. By leveraging the collective intelligence of distributed data sources, this framework seeks to improve cancer diagnosis accuracy while maintaining appropriate security measures for sensitive patient data. In the proposed system, the quasi-identifiers
are identified as independent attributes in health data, while the others are classified as confidential information (CI). Consequently, to enhance security and privacy
differential privacy anonymization is performed on the
attributes, and the resulting data is mixed with the CI attribute. Using anonymized data, the Cramer GAN was trained using Cramer distance for efficiency, and privacy was assessed. Notably, the suggested architecture obtains diagnosis accuracy of 97.80 % for lung cancer, 96.95 % for prostate cancer, and 97 % for breast cancer. Based on the experimental analysis, the proposed architecture detects cancer accurately and addresses security and collaboration issues. This paradigm has the potential to transform healthcare and improve patient outcomes globally.