# AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

arXiv: https://arxiv.org/abs/2512.16455 · HTML: https://mlsanity.com/paper/2512.16455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 105 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2](https://mlsanity.com/paper/2502.03544/) (2025)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Provenance Tracking in Large-Scale Machine Learning Systems](https://mlsanity.com/paper/2507.01075/) (2025)
- [Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas](https://mlsanity.com/paper/2408.05761/) (2024)
- [Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data](https://mlsanity.com/paper/2501.15949/) (2025)
- [Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks](https://mlsanity.com/paper/2502.01352/) (2025)
- [INDIGO-DataCloud:A data and computing platform to facilitate seamless access to e-infrastructures](https://mlsanity.com/paper/1711.01981/) (2017)
- [Orchestrating Complex Application Architectures in Heterogeneous Clouds](https://mlsanity.com/paper/1711.03334/) (2017)
- [A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications](https://mlsanity.com/paper/2211.07738/) (2022)

## Abstract (arXiv)

The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper presents AI4EOSC, a federated, open-source platform designed to operationalize the full AI/ML lifecycle within the European Open Science Cloud (EOSC) ecosystem. Our methodology tackles the fragmentation of distributed research infrastructures by integrating a modular and distributed architecture comprising an AI development platform, a serverless AI-as-a-Service layer, and a federated orchestration model that is able to integrate heterogeneous compute and storage resources from distributed e-Infrastructures. AI4EOSC also introduces a ``FAIR-by-design'' approach that enforces metadata standardization (via MLDCAT-AP) and W3C PROV-compliant provenance tracking through a platform-integrated CI/CD pipeline. AI4EOSC added value is demonstrated through the delivery of a diverse set of community installations, showing consistent and seamless deployment across heterogeneous cloud providers. These installations are validated by a set of scientific cases, showing how our work reduces the manual burden on researchers while ensuring high levels of reproducibility and interoperability and providing an unified environment for development, training, and production of AI/ML models in the EOSC.