Federated and distributed inference leveraging sensing and communication in the computing continuum

Información del proyecto

FIND-OUT

Identificador del acuerdo de subvención: 101078411

DOI

10.3030/101078411

Fecha de la firma de la CE 4 Mayo 2023

Fecha de inicio 1 Julio 2023

Fecha de finalización 30 Junio 2028

Financiado con arreglo a

• European Research Council (ERC)

Coste total

€ 1 019 000,00

Aportación de la UE

€ 1 019 000,00

Coordinado por

UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA
Italy

Periodic Reporting for period 1 - FIND-OUT (Federated and distributed inference leveraging sensing and communication in the computing continuum)

Período documentado: 2023-07-01 hasta 2025-12-31

Resumen del contexto y de los objetivos generales del proyecto

The integration of sensing and communication is attracting a fervent research activity and will result in a myriad of contextual data that, if properly processed, may enable a better understanding of local and global phenomena while increasing the quality, security, and efficiency of our ecosystems. The computing continuum offers a timely and unique solution for processing such a massive volume of sensed data, as it provides virtually unlimited and widely distributed computing resources. Nevertheless, the deployment of data analysis at the edge or in the cloud has many implications regarding latency, privacy, security, and data integrity. As we learn how to sense ubiquitously and we build a tool able to handle the sensed data, the greatest challenge is to understand how and where to process them. The purpose of this project is the development of a pioneering framework to guide the design of federated and distributed inference systems, leveraging sensing and communication and harnessing the computing continuum. The framework will build on: (i) the definition of statistical and mathematical models for the sensed data, which capture the complex and interrelated phenomena underpinning sensing and communication systems, with different levels of integration; (ii) the development of cloud-native inference algorithms, mainly distributed and parallelized, with scalable complexity that can be adapted to dynamic performance requirements; (iii) the design of orchestration strategies to guide the flexible deployment of the inference process at the edge and in the cloud with a dynamic allocation of the computing resources. The aim is to overcome the paradigmatic accuracy-complexity trade-off that has driven distributed inference for decades, leading to a paradigm shift that encompasses multi-level performance indicators beyond accuracy, including latency, integrity, privacy, and security aspects and how these impact the confidence on the inferred phenomena.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

This project explores how future wireless networks can not only connect devices but also “sense” their surroundings, creating a richer picture of the world and enabling more reliable, efficient, and secure digital services. The key idea is to integrate sensing and communication into a single system, and to harness the vast computing resources available across devices, the edge, and the cloud to make sense of the data.

During the first reporting period, the project advanced on four fronts:

- New models for sensing data. We developed methods to describe how sensing information is shaped by both the physical environment and the network itself. These models make it possible to bring together very different types of data in a consistent way, providing the foundation for advanced inference.

- Understanding system performance. We investigated how accuracy, speed, privacy, and security interact in integrated sensing and communication. This included studying how systems behave under malicious interference and developing new indicators that go beyond accuracy alone to assess the trustworthiness of results.

- Cloud-based inference algorithms. We designed and tested algorithms that can run efficiently across distributed cloud and edge computing environments. These algorithms adapt to changing conditions, reduce the amount of data that needs to be exchanged, and protect privacy through federated learning.

- Resource orchestration. Using a testbed that combines simulation, radio hardware, and cloud services, we showed how sensing and communication tasks can be managed together. This demonstrated how different design choices affect accuracy, speed, and resilience, and provided a foundation for more advanced resource management.

These initial results show that integrated sensing and communication can be realized in ways that enhance privacy, security, and efficiency. These achievements lay the groundwork for a new generation of wireless networks that can both communicate and perceive their environment, with potential benefits for transportation, industry, and society at large.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

At this stage of the project, the core scientific objectives have been translated into concrete methodologies. The achievements already demonstrate feasibility and highlight the potential of integrated sensing and communication to transform wireless networks.