Policy-Aware Federated Query Orchestration Across Energy Data Spaces and Edge AI Services

Abstract

The execution of AI driven analytical tasks across distributed data sources and edge services remains challenging due to heterogeneous governance policies, computational constraints, and volatile edge environments. In this demonstration, we present a modular federated orchestration framework for governance-aware query processing in smart grids. The system maintains a continuously updated context model of energy assets, computational resources, and participating entities, enabling policy-aware planning. Natural language operator intents are translated into executable federated workflows using large language models. A custom energy data space connector enforces local edge data policies while supporting secure multi-node data exchanges and associated computation. A federated orchestrator coordinates distributed workflow execution, ensuring policy compliance, infrastructure feasibility, and adaptive re-optimization under dynamic conditions.