Context
Across Europe, connected equipment plays an increasingly important role in ensuring efficient, reliable and sustainable operation in both industrial and field environments. These devices often operate in customer contexts where operational data is confidential, OEM access is restricted, and streaming all data to the cloud is not practical or desirable. As a result, traditional centralised machine-learning approaches are difficult to apply in real operational settings. Increasingly, these devices rely on machine-learning models that must be monitored and improved over time to remain reliable. At the same time, Europe is pursuing improvements in efficiency, sustainability and resilience in line with initiatives such as the Green Deal and Industry 5.0 creating a strong need for practical, decentralised AI solutions.
Overall Objectives
The Ekkono FLIIoT project set out to develop Synthesis, a system for managing machine-learning models directly on distributed devices without requiring access to raw data. Synthesis was designed to keep models up to date in the field by enabling confidentiality-preserving learning through federated and model-centric techniques, automatically detecting anomalies, under-performing devices and model degradation, providing meaningful fleet-level insights while keeping data at the edge, and delivering a scalable, intuitive interface capable of handling thousands of models in real time. These objectives support more efficient, resilient and sustainable operation across diverse deployments.
Project Pathway to Impact
Synthesis progressed from core model-centric and federated learning techniques to a scalable platform capable of operating across varied real-world contexts. The completed system was validated in two major sectors: manufacturing and industrial equipment and energy and thermal systems. It demonstrated its capability to support improved operational reliability and efficiency, with promising indications of consistent long-term performance. By enabling these capabilities without transferring raw operational data, Synthesis addresses practical constraints faced by OEMs and operators. The resulting impact includes reduced downtime, better resource use and more informed operational decision-making, contributing to more resilient, efficient and sustainable systems in line with Europe’s broader transition goals.
