Advancing AI-Cloud Integration: Comparative Analysis of Algorithms and Novel Solutions

The integration of Artificial Intelligence (AI) and Cloud Computing has revolutionized industries through scalable, intelligent systems. However, existing algorithms face challenges in security, privacy, and data integrity, limiting their efficacy. This paper critically evaluates 10 state-of-the-art algorithms (2018–2023) for AI-cloud integration, identifying gaps in encryption, resource optimization, and edge- AI coordination. We propose a Federated Quantum- Resistant Encryption Algorithm (FQREA) that combines federated learning with lattice-based cryptography to address vulnerabilities in existing frameworks. Our analysis reveals that traditional methods like Homomorphic Encryption (HE) and Differential Privacy (DP) incur 25–40% latency overheads, while centralized cloud-AI architectures exhibit 30% higher vulnerability to adversarial attacks. In contrast, FQREA reduces inference latency by 18% and improves data integrity by 35% through decentralized trust mechanisms. Case studies in healthcare and finance demonstrate FQREA’s superiority, achieving 99.2% accuracy in federated medical diagnostics while reducing data leakage by 62%. Performance metrics across security, privacy, and integrity are benchmarked against existing models, with FQREA outperforming in 6/8 categories. This work bridges the research gap in scalable, secure AI-cloud systems and provides a pathway for quantum-ready architectures.