Privacy-Preserving AI: Federated Learning, Differential Privacy, and Data Minimization: Production Architecture, Current Research, Real-World Use Case, Implementation, and Verification
Abstract
Privacy-Preserving AI: Federated Learning, Differential Privacy, and Data Minimization: Production Architecture, Current Research, Real-World Use Case, Implementation, and Verification Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Affiliation: Capgemini US LLC, Chicago, IL, USA Credentials: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Representative Production Use Case:Multiple hospitals improve a shared prediction model without centralizing raw patient data. Executive Abstract & Architecture Scope:This publication delivers a 22-page comprehensive, production-ready enterprise AI architecture blueprint covering 21 structured engineering sections: Executive Summary, State of Technology in 2026, Business Requirements, Component Architecture, Data and Context Engineering, Integration Protocols, Zero-Trust Security, Governance, Resilience, Observability, Performance Engineering, FinOps Cost Optimization, Operating Sequences, Implementation Code Patterns, Testing/Validation, Failure Modes, Phased Adoption Roadmap, and Production Readiness Checklist. Series: AI Technology Whitepapers (Paper 30 of 50)