Federated Deep Learning Frameworks For Privacy-Preserving Medical Data Analysis Using Multi-Modal AI Models

Abstract

This paper presents a state-of-the-art Federated Deep Learning (FDL) architecture that can support privacy-preserving medical data analysis on an AI multi-modal structure, combining MRI scans, CT images, and Electronic Health Records (EHRs) to arrive at a full diagnostic intelligence. The suggested FDL framework allows hospitals to collaboratively train securely models without the need to move sensitive patient data to a centralized location, unlike centralized deep learning, where this data must be aggregated in a single place, its total locality must be ensured, and the strictest medical privacy policies must be followed. Experimental analyses prove that the suggested multi-modes FDL model is characterized by a better classification accuracy than both centralized and traditional federated models, and a significant improvement can be observed in the integrated multi-modal analysis. The use of sophisticated optimization techniques such as gradient quantization, sparse updates and adaptive client selection minimizes the communication costs by up to 75 and hence the framework is highly scalable in case of hospitals with limited computation or network capabilities. Also, layered privacy-sensitive approaches, including Differential Privacy, Secure Aggregation, and Randomized Noise Injection support the drastic reduction of member inference, gradient inversion, and reconstruction attacks, which guarantee a high level of privacy leakage protection. In general, the results show that the suggested framework is a safe, effective, and high-performing solution to a healthcare setting in the real-world that aims to implement collaborative AI technologies without jeopardizing patient confidentiality.