New approaches for decentralized, federated and sustainable AI data processing (RIA)
Forthcoming
Status as published by the data source.
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 proposed developments in at least two complementary use cases in different domains. These use cases should demonstrate the value gained and potential impact of project achievements in real-world situations, as well as address key applications and sectors critical to Europe's competitiveness. Use cases should provide compelling examples and scenarios and cater for the reproducibility of results' added value and impact in additional economic sectors.
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Activities are expected to start at TRL 3 and achieve TRL 6-7 by the end of the project – see General Annex B.
- Status
- Forthcoming
- Deadline
- (time not stated)
- Opens
- Published
- Total budget
- €35,000,000
- Grant range
- €17,500,000 – €17,500,000
- Country
- European Union (EU-wide)
- Programme
- Horizon Europe (HORIZON)
- Official page
- Open at the source portal
Other calls under Horizon Europe (HORIZON)
- PV based electrification of the economy: Designing & optimising PV systems supporting industrial electrification and promoting participation in electricity markets (EUPI-PV Partnership)
- Advanced TSO control rooms to enhance grid observability, stability and resilience
- Advanced Distribution Management Systems (ADSM) for more efficient and flexible distribution grids
- Community of practice - Data-Driven Decision-Making in Energy
- Industrial processes and equipment for innovative, reliable and scalable tandem technologies (EUPI-PV Partnership)
- Integrated Approaches for Retrofitting Infrastructures with Innovative Energy Storage Technologies
- Demonstration of hydropower technologies for efficient and forward-looking refurbishment of existing hydropower plants
- Delivery of industrial CCUS clusters – Societal Readiness pilot
All calls under this programme
Similar opportunities
- Energy efficiency and sustainability of AI data processing in Data Centres (IA)
- EU Frontier AI Initiative: Developing frontier AI solutions that are safe and computationally efficient within Apply AI (RIA)
- New approaches for Human/AI collaboration for the workforce of the future (RIA) (Made in Europe and AI, Data and Robotics partnerships)
- International cooperation in AI (IA)
- Apply AI: Challenge-Driven AI Innovation Booster in Apply AI prioritised sectors (RIA) (Partnership in AI, Data and Robotics)
- Explainable and Robust AI (AI Data and Robotics Partnership) (RIA)
Where this came from
- Source document
- https://ec.europa.eu/info/funding-tenders/opportunities/data/topicDetails/horizon-cl4-2027-04-data-03.json
- Document fingerprint
a6b6a549567b5db0(SHA-256, first 16 hex characters)- Retrieved
- First recorded here
What has changed
- url first recorded as https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl4-2027-04-data-03 on
- currency first recorded as EUR on
- amount_max first recorded as 17500000.00 on
- amount_min first recorded as 17500000.00 on
- budget_total first recorded as 35000000.00 on
- deadline first recorded as 2027-03-18 on
- opens_on first recorded as 2026-11-17 on
- published_on first recorded as 2025-12-12 on
- status_basis first recorded as source_status on
- status first recorded as forthcoming on
- title first recorded as New approaches for decentralized, federated and sustainable AI data processing (RIA) on
Data source
© European Union, 2026. Source: EU Funding & Tenders Portal. Reused under Commission Decision 2011/833/EU — CC BY 4.0.
Retrieved from the source on .
The source last updated this document on Mon, 02 Mar 2026 09:46:38 GMT.
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