Federated Learning Economics: Privacy vs Efficiency

After seven years of implementing AI systems across healthcare, finance, and enterprise domains, I have observed a fundamental tension in modern machine learning: organizations need data to build effective models, but privacy regulations, competitive concerns, and ethical considerations prevent centralized data collection. Federated learning promises to resolve this paradox by training models across distributed datasets without moving the data itself. This article examines the complete economic picture: infrastructure costs, communication overhead, convergence efficiency, regulatory compliance savings, and the strategic value of privacy-preserving AI.