On the Edge of Tomorrow: A Validated Blueprint for Deploying Federated Learning on Industrial Edge Hardware
Engineering intelligent systems for industrial edge device fleets is often hindered by the non-functional requirements of data privacy, transmission cost, and client heterogeneity, which make traditional centralized architectures impractical. This paper presents a generalizable architectural blueprint designed to address these constraints for resource-constrained, time-series forecasting tasks. The blueprint is founded on a dual-adaptation mechanism that combines a longterm, asynchronous Federated Learning (FL) protocol with a lightweight, on-device correction mechanism for rapid, real-time adaptation. To validate this blueprint, we apply it to the challenging use case of real-time State of Charge (SoC) prediction for a heterogeneous fleet of commercial Battery Electric Vehicles (BEVs) and the NASA C-MAPSS dataset to evaluate Remaining Useful life (RUL) of jetengines. The system was implemented and evaluated on production-intent embedded ARM hardware. The results demonstrate that the blueprint enables a privacypreserving federated system to achieve a final prediction error nearly on par with a non-private, fully centralized model. In the SoC experiment, the client with the least amount of data, reduced the median end-of-trip error from over 4% in a local-only model to under 1.5% while the RUL experiment reduced the local model median error of 5.85 cycles down to 0.16 using the blueprint. This work provides a definitive blueprint for deploying effective, privacy-preserving AI systems in industrial embedded systems, proving that a thoughtful engineering approach allows performance and privacy to coexist without compromise.