# Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0

arXiv: https://arxiv.org/abs/2403.14650 · HTML: https://mlsanity.com/paper/2403.14650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.12062/) (2021)
- [Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques](https://mlsanity.com/paper/2305.10118/) (2023)
- [On the utility and protection of optimization with differential privacy and classic regularization techniques](https://mlsanity.com/paper/2209.03175/) (2022)
- [POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification](https://mlsanity.com/paper/2212.06735/) (2022)
- [POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique](https://mlsanity.com/paper/2210.02959/) (2022)
- [Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks](https://mlsanity.com/paper/2306.03054/) (2023)

## Abstract (arXiv)

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.