Federated Learning for Official Statistics: A Systematic Review of Privacy‐Preserving Architectures and the Path Toward GSBPM Integration

National Statistical Offices (NSOs) face an escalating “privacy‐utility” impasse, where the demand for high‐granularity data must compete with the near zero risk tolerance of legal frameworks like the General Data Protection Regulation (GDPR). Traditional centralized collection creates “honeypots” for breaches, exposing a nation's entire digital census or tax records. This paper provides a systematic review of Federated Learning (FL) as a decentralized framework that eliminates raw microdata consolidation. Drawing on 77 peer‐reviewed. Studies and technical reports published between 2020 and 2026, the study evaluates Horizontal and Vertical FL architectures alongside Privacy‐Enhancing Technologies (PETs). The analysis reveals that while PETs introduce a “utility gap,” Under controlled experimental settings and standardized non‐IID configurations, the evaluated architectures frequently achieve utility bounds exceeding 90% of their centralized equivalents, though this outcome varies based on the assigned privacy budget () and data distribution. Crucially, the study demonstrates that the mathematical noise required for Differential Privacy (DP) can threaten strict accuracy thresholds; for example, an aggressive privacy budget can cause census distributions to shift more than 77% from the true mean, rendering indicators like Gross Domestic Product (GDP) or unemployment rates statistically meaningless. Beyond these accuracy trade‐offs, the paper addresses the Non‐Independent and Identically Distributed (Non‐IID) nature of government datasets and identifies research gaps in standardized audit trails. The paper proposes a novel roadmap for integrating FL into the Generic Statistical Business Process Model (GSBPM), offering NSOs and international statistical bodies a technically grounded and legally coherent pathway toward next generation data infrastructure.