mlsanityData to 14 Oct 2026

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

arXiv 2512.16455 ↗First posted 18 Dec 2025cs.DC cs.AIcited by 0

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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.

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.

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17 of 105 references.

References · 17

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5 more
  1. 2025Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Datacited by 2
  2. 2025Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attackscited by 2
  3. 2017INDIGO-DataCloud:A data and computing platform to facilitate seamless access to e-infrastructurescited by 1
  4. 2017Orchestrating Complex Application Architectures in Heterogeneous Cloudscited by 1
  5. 2022A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applicationscited by 1

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