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.

This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.