Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0
Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi, Francesc Lordan, Fernando Vazquez, Eugenio Lomurno, Alberto Archetti, Danilo Ardagna, Matteo Matteucci
TL;DR
The paper tackles the challenge of deploying AI across the computing continuum from cloud to edge in multiple critical domains. It presents the AI-SPRINT framework, incorporating Studio and Runtime tools (e.g., PyCOMPS/dislib, POPNAS, SGDE, SPACE4AI, OSCAR, SCONE) to optimize performance, energy efficiency, accuracy, and privacy within security-enhanced, privacy-preserving workflows. Three concrete use cases—Personalized Healthcare, Maintenance and Inspection, and Farming 4.0—demonstrate GDPR-compliant data collection, real-time or near-real-time processing, and substantial data-transfer reductions, backed by NAS-driven model optimization and synthetic data strategies. The findings highlight the practicality and impact of distributed ML and continuum-aware orchestration for real-world AI deployment, offering actionable lessons for scalable, secure AI across edge-cloud environments.
Abstract
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications -- Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 -- highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains. After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned.