Design of a Federated Multimodal AI-Driven E-Commerce Ecosystem Employing Quantum-Safe Personalization to Optimize Conversion and Retention Metrics

This paper presents a pioneering framework for next-generation e-commerce that integrates federated multimodal artificial intelligence with quantum-safe personalization techniques to optimize user conversions and retention rates. By enabling decentralized training across client devices on diverse data modalities including text, images, videos, and behavioural signals the system generates contextually rich recommendations without centralizing sensitive user data, thereby upholding privacy standards like GDPR. Quantum-resistant cryptographic protocols, such as lattice-based encryption, safeguard model updates against emerging quantum threats, while adaptive algorithms dynamically refine personalization to boost immediate purchase likelihood and long-term engagement. Extensive simulations on large-scale e-commerce datasets demonstrate superior performance, achieving up to 30% gains in conversion metrics and 25% improvements in retention compared to traditional centralized or non-quantum approaches, paving the way for scalable, secure AI deployment in retail.