Our approach will re-engineer distributed function computation over networks to incorporate various multi-user communication constraints and capabilities, particularly in multicast, broadcast, and multi-access environments. By blending tools from coding theory, graph theory, source and channel coding, compressive sensing, and task-aware communication, we aim to achieve:
• A multiplicative factor reduction in communication volume over existing techniques.
• Greater energy efficiency and reduced infrastructure cost, essential for sustainable next-generation wireless deployments.
The recent evolution of parallel and distributed algorithms to process big data sets and large language models has exposed the crucial role of communication-computation co-design. Thus, scalable computation frameworks should simultaneously capture encoding, processing, and coordination strategies for the computation of various tasks abstracted by functions. Our vision is to tackle the growing heterogeneous demand by exploiting
• Decentralized topologies with mixed multicast/unicast patterns.
• Correlated data and side information across devices.
• Sparse computation tasks, and function-oriented evaluations.
This calls for a fundamentally new theoretical and practical framework that can capture the recent trends and emerging architectures to be supported by communication systems. In this space, our work will address:
• Exploitation of data correlation, function structure, and topology to reduce transmission load.
• Straggler mitigation and device heterogeneity, critical in real-world deployment scenarios.
• Novel coding and representation strategies for sparse and function-oriented computing.
Our research aligns with Europe’s and the global community’s priorities for green digital transformation, sovereign AI infrastructure, and equitable access to intelligence. Over the next five years, we will pursue these goals through collaborative projects, open testbed development, and targeted contributions to international standardization efforts.
