Federated Learning for Secure and Privacy-Preserving Medical Image Analysis in Decentralized Healthcare Systems

The need for safe and effective ways to manage private patient information has grown in tandem with deep learning algorithms in medical image analysis. Preserving patient privacy while utilizing collective intelligence for accurate diagnostic models is difficult in decentralized healthcare systems when medical data is disseminated across many organizations and locations. This study suggests a federated learning architecture to analyze medical images in decentralized healthcare systems safely and privately. The suggested method uses federated learning concepts to enable several healthcare organizations to work together in training a deep learning model, all while keeping patient data private. Regarding data privacy and security, each institution handles things on its own. Medical image analysis can benefit from developing strong and precise deep learning models through federated learning's decentralized model training approach. The federated learning framework uses differential privacy measures, secure communication protocols, and advanced encryption techniques to guarantee privacy and security. During the training of the model, these safeguards protect sensitive medical data. The study also investigates how the privacy-preserving features of the suggested system might be improved by using federated averaging and homomorphic encryption. Various healthcare institutions' real-world medical imaging datasets are used to evaluate the federated learning system experimentally. The results show that the method can achieve centralized model performance levels while keeping data private and secure with 98.6% accuracy. Model convergence, communication overhead, and computational efficiency are other consequences of federated learning examined in the paper.