Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems

The convergence of federated learning and hybrid cloud computing represents a transformative paradigm for privacy-preserving data intelligence. This review examines federated learning implementations in hybrid cloud environments, analyzing security mechanisms, privacy-preserving capabilities, and scalability challenges. We explore architectural frameworks and deployment strategies while analyzing security and privacy challenges from technical, organizational, and regulatory perspectives. The study highlights synergistic benefits of combining federated learning with hybrid cloud infrastructure and discusses emerging trends including homomorphic encryption, differential privacy, and blockchain integration. Through comprehensive literature analysis of publications from 2016 to 2024, key findings reveal that federated learning in hybrid clouds offers unprecedented opportunities for privacy-preserving analytics while introducing unique challenges in communication efficiency and cross-environment orchestration. Organizations can effectively leverage federated learning by implementing layered security architectures and maintaining continuous adaptation to evolving privacy regulations. This analysis provides valuable insights for practitioners and researchers navigating the intersection of federated learning and hybrid cloud computing.