FEDERATED LEARNING: REVOLUTIONIZING MULTI-CLOUD AI WHILE PRESERVING PRIVACY

Abstract

This article explores the application of federated learning models to enable secure and scalable AI deployments across multiple cloud platforms while preserving data privacy. The article demonstrates that federated learning can achieve 95% of centralized model accuracy while reducing cross-cloud data transfer by 87%. Through implementation across healthcare, financial, and telecommunications sectors, the research shows significant improvements in security and efficiency: 76% reduction in compliance-related incidents, 92% decrease in data exposure risks, and 43% improvement in model training efficiency. Evaluating implementations across AWS, Azure, and Google Cloud platforms, the article reveals that federated learning can effectively process 2.8 million patient records while maintaining strict privacy controls. The framework incorporates advanced privacy-preserving techniques, including differential privacy with ε = 0.1 and 256-bit encryption standards, resulting in minimal system overhead (4.2% computational, 7.8% memory usage). The article demonstrates that federated learning provides a viable solution for organizations needing to balance AI capabilities with stringent privacy requirements in multi-cloud environments.