New approaches for decentralized, federated and sustainable AI data processing (RIA)

Developing an agile and secure single market for data and trustworthy AI services is central to Europe’s digital sovereignty and competitiveness. The convergence of the Telco-Edge-Cloud continuum (3C) with open orchestration platforms will unlock the transformative potential of AI across strategic sectors, from mobility and energy to health and manufacturing, fostering new services and business models. Building a sovereign Open Internet Stack, rooted in open-source, interoperable and standard-based solutions, will reinforce trust, resilience and innovation, while ensuring Europe retains control over critical digital infrastructures. At the same time, decentralised and federated approaches to AI data processing, combined with breakthroughs in sustainable data centres, will help overcome Europe’s compute bottlenecks and dependencies, and reduce the environmental footprint of AI. By aligning with the Data Union Strategy and Common European Data Spaces, these efforts will deliver secure, compliant and adaptive data-sharing frameworks that empower citizens, businesses and administrations. Together, they will strengthen Europe’s ability to innovate, scale and lead globally in data and AI, anchoring digital sovereignty in line with EU values and strategic interests.

Legal entities established in China are not eligible to participate in both Research and Innovation Actions (RIAs) and Innovation Actions (IAs) falling under this destination. For additional information please see “Restrictions on the participation of legal entities established in China” found in General Annex B of the General Annexes.

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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