Quantum-Resilient Federated Cognitive Networks for Secure and Ethical AI-Powered Mental Health Interventions

Abstract

Integrating Artificial Intelligence (AI) into interventions for mental health issues significantly augments both personalization and access to care. Still, the challenges of protecting extremely sensitive patient data and the highly probable and available quantum computers security threat have plagued the mental health care initiatives for expansion. Centralized training and learning models for AI seem especially prone to catastrophic loss of data as well as catastrophic decryptions of documents once quantum computers with cryptographically relevant computational powers become available, making all the contemporary safeguards pointless. These and similar issues have spawned the development of this document, which outlines what we deem as the novel and interdisciplinary paradigm of the Quantum–Resilient Federated Cognitive Network (QRFCN), which we propose as the primary solution. Ethical data sovereignty necessitates the retention of sensitive diagnosis and cognitive assessment information on patient devices or on data residing with health care institutions, and QRFCN employs Federated Learning (FL) to accomplish this. Most importantly, QRFCN employs Post-Quantum Cryptography (PQC) using the lattice-based Kyber key encapsulation mechanism and Dilithium digital signature scheme to server-side supplementary proxy communications to preserve custody of all FL aggregation inter-client and inter-server communications. Then comes the introduction of QR-FedAvg, the augmented federated averaging quantum-resilient model of disaggregated, modular, localized server-client proxies, the first PQC sensitive communications overhead probable system model that seeks to the latent PQC super ratioed cognitive disaggregating tensor. The simulated Non-IID clinical dataset under study shows that QRFCN captures model utility (91.3% accuracy) similar to centralized and classical FL approaches while concurrently providing strong protection against known quantum attacks. This research resolves the gap concerning offering a plausible, safe, and ethical method of next-generation AI deployment in sensitive domains of healthcare.