Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications

Abstract

The domain of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information's privacy and security - a monumental task that creates significant obstacles. This paper presents an innovative approach designed to address these challenges through the implementation of privacy-preserving federated learning models, effectively pioneering a novel path in this intricate field of research. Our proposed solution enables healthcare institutions to collectively train machine learning models on decentralized data, concurrently preserving the confidentiality of individual patient data. During the model aggregation phase, the proposed mechanism enforces the protection of sensitive data by integrating cutting-edge privacy-preserving methodologies, including secure multi-party computation and differential privacy. To substantiate the efficacy of the proposed solution, we conduct an array of comprehensive simulations and evaluations with a concentrated focus on accuracy, computational efficiency, and privacy preservation. The results obtained corroborate that our methodology surpasses competing approaches in providing superior utility and ensuring robust privacy guarantees. The proposed approach encapsulates the feasibility of secure and privacy-preserving collaboration on healthcare data, serving as a compelling testament to its practicality and effectiveness. Through our work, we underscore the potential of harnessing collective intelligence in healthcare while maintaining paramount privacy protection, thereby affirming the promise of a new horizon in collaborative healthcare informatics.