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

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. null Activities are expected to start at TRL 3 and achieve TRL 6-7 by the end of the project – see General Annex B.

• See official call conditions for eligibility

• 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.