Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification

Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of privacy concerns and security threats in healthcare, federated learning (FL) has emerged as a promising approach to enable collaborative model training across decentralized datasets without exposing sensitive patient information. However, FL remains vulnerable to advanced security breaches such as gradient inversion and eavesdropping attacks. This paper introduces a novel framework that integrates federated learning with quantum-inspired encryption techniques for dementia classification, emphasizing privacy preservation and security. Leveraging quantum key distribution (QKD), the framework ensures secure transmission of model weights, protecting against unauthorized access and interception during training. The methodology utilizes a convolutional neural network (CNN) for dementia classification, with federated training conducted across distributed healthcare nodes, incorporating QKD-encrypted weight sharing to secure the aggregation process. Experimental evaluations conducted on MRI data from the OASIS dataset demonstrate that the proposed framework achieves identical accuracy levels to a baseline model while enhancing data security and reducing loss by almost 1% compared to the classical baseline model. The framework offers significant implications for democratizing access to AI-driven dementia diagnostics in low- and middle-income countries, addressing critical resource and privacy constraints. This work contributes a robust, scalable, and secure federated learning solution for healthcare applications, paving the way for broader adoption of quantum-inspired techniques in AI-driven medical research.