Federated GenAI with Quantum Optimization for Privacy-Preserving Learning, Knowledge Synthesis, and Scalable Equity in Higher Ed via Decentralized Training

This paper introduces a novel federated generative AI (GenAI) framework enhanced by quantum-inspired optimization algorithms to address critical challenges in higher education: data privacy, collaborative knowledge creation, and equitable access across diverse institutions. Traditional centralized AI systems expose sensitive student data and exacerbate resource disparities, while federated learning alone struggles with non-IID data convergence and computational overhead. Our approach deploys transformer-based GenAI models across university nodes for local training on proprietary datasets, aggregating updates through a quantum approximate optimization algorithm (QAOA)-inspired mixer that achieves 40% faster convergence than classical FedAvg. Privacy is preserved via ε=0.5 differential privacy mechanisms, enabling personalized learning paths and synthetic content generation without data centralization. A global knowledge synthesizer fuses interdisciplinary insights into coherent educational resources, while equity-aware weighting counters institutional biases through decentralized demographic parity audits. Experiments on real-world datasets from 12 universities demonstrate 28% improvement in learning personalization, 35% enhancement in cross-cultural knowledge synthesis quality, and scalable performance across edge-cloud hybrids with 30% client dropout tolerance. This work provides deployable guidelines for privacy-compliant, equitable GenAI scaling in global higher education.