Privacy-Aware Federated Learning Model for Medical Data Analysis

The difficulty of utilizing dispersed patient data while upholding stringent privacy compliance has become critical in an era of healthcare that is becoming more and more data-driven. Conventional centralized analytics techniques violate patient privacy and encounter major legal obstacles under laws like GDPR and HIPAA. A thorough federated learning architecture created especially for healthcare analytics is presented in this research, allowing several healthcare organizations to work together to train machine learning models without disclosing private patient information. To protect data while preserving model accuracy and clinical utility, our technology uses sophisticated privacy-preserving approaches like homomorphic encryption, secure aggregation, and differential privacy.The suggested system supports a variety of healthcare datasets, including medical imaging data and electronic health records, and has a web-based interface that democratizes access to federated learning capabilities. Through thorough tests on actual healthcare datasets, we show the platform's efficacy in reaching competitive performance metrics while protecting patient privacy. Because of the system's automated feature engineering, model selection, and data pretreatment, healthcare professionals can use federated learning without needing extensive technical knowledge. Our findings open the door for the broad use of collaborative healthcare analytics by demonstrating that federated techniques can achieve up to 95% of centralized model performance while offering strong privacy assurances.