Distributed Intelligence and privacy‑preserving deployment: Edge–Cloud–6G–Federated Learning for Secure, Auditable Decision Support

As distributed intelligence becomes integral to high‑stakes decision support, concerns about privacy, security, latency and governance intensify. This title‑driven scoping review analyses publications spanning cybersecurity, energy, healthcare, finance, public welfare and agriculture to map the edge–cloud–6G–federated continuum. Titles suggest archetypes ranging from cloud‑centric management information systems to edge‑first inference and low‑latency healthcare deployments, hybrid orchestration and 6G zero‑touch networks, and privacy‑first federated learning frameworks. The review infers threat models (membership inference, model inversion, poisoning, gradient leakage, adversarial examples) and explores privacy‑preserving mechanisms such as secure aggregation, differential privacy, encryption and blockchain integrity. Sectoral synthesis highlights critical infrastructure cyber intelligence, smart grids and renewable energy forecasting, privacy‑first clinical decision support, fraud prevention pipelines, and welfare governance models. A deployment taxonomy, threat matrix, privacy mechanism trade‑off analysis and governance checklist are provided. The review culminates in a research agenda addressing standardised threat modeling, joint optimisation of privacy–utility–latency–energy, explainability for distributed pipelines, governance automation and continual learning under compliance constraints. By offering a unified framework for distributed AI assurance and highlighting gaps, this scoping synthesis lays the groundwork for evidence‑based evaluation when full texts are available.