New approaches for decentralized, federated and sustainable AI data processing (RIA)
eu HORIZON-CL4-2027-04-DATA-03 · Horizon Europe (HORIZON)
| Status | forthcoming |
|---|---|
| Opens | 17 Nov 2026 |
| Deadline | 18 Mar 2027 — 187 days |
| Action | HORIZON Research and Innovation Actions |
| Official page | https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunit… |
| Fetched | 2026-09-12 04:00:03+00:00 |
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Expected Outcome: Project results are expected to contribute to developing new approaches, tools and techniques that overcome the obstacles of today's centralised AI compute techniques: limits in the availability of energy and AI compute capacity in centralised standalone environments, limited availability of types of AI chips, data quality and security and latency in AI data processing. The ultimate objective is to help overcome EU’s AI compute capacity bottlenecks by offering alternative decentralised and sustainable AI compute models that enable exploitation of diverse hardware processing architectures and scaling approaches. Scope: This topic focusses on technologies and techniques that enable AI data processing to leverage distributed compute resources across the cloud and edge computing continuum throughout the whole AI model lifecycle from data collection, training, fine-tuning, and deployment. To overpass today’s state of the art in the area, the considered research areas include: To research on distributed, decentralised, and federated “compute continuum” enabled AI architectures beyond federated learning and integrating model compression tools and new mechanisms to enable AI data processing to scale across multiple and diverse computing infrastructures. Development, deployment, and operation of AI workflows across heterogeneous and distributed infrastructures along the compute continuum (edge, cloud, HPC), including the possibility of incorporating innovative computing paradigms (neuromorphic and quantum computing) and hardware efficiency enhancements ((e.g., including in-memory computing, and hardware and software approximation). Novel methods and techniques to improve data availability and consistency for decentralised AI data processing. These consider tools to ensure data quality (e.g. prevention of data sets imbalance or inconsistency across distributed data sources), volume optimisation for data transfers across environments, and distributed data management, all while preserving data privacy and preventing data leaks (e.g. via advanced cryptographic protection such as post-quantum cryptography for resistance to emerging quantum threats). New tools and mechanisms to measure, monitor and improve end-to-end energy efficiency and sustainability of AI data processing across the compute continuum, including the exploration of energy and sustainability implications of the heterogeneous AI processing architectures and their impact in the compute infrastructure design and long-term sustainability. Successful project proposals should showcase propo…
What consortia for this call have looked like
2 projects
have been funded under this line
(HORIZON-CL4-DATA-03) in Horizon Europe and H2020. This is what they
were made of — not a recommendation, just what the Commission signed.
range 13–13
11 seen across all of them
across those projects
The mix of organisations
Who coordinated them
Coordinating an EU project is a demanding, specific job. These organisations have done it on this topic line before.
This is the shape, not the consortium. It says how many partners these projects had and what kinds of organisation were in them. It does not know whether your idea needs the same mix, who is available, who is already committed elsewhere, or how the work and the budget should be divided. Those are judgement calls on a specific proposal, and no amount of public data answers them.
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