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FLASH

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FLASH - Federated Learning Supporting Efficient and Reliable Inference over Vehi...

FLASH - Federated Learning Supporting Efficient and Reliable Inference over Vehicular Networks The FLASH project aims to establish the theoretical foundations of machine learning and wireless communications that will enable the vision of assisted and self-driving systems. Unfortunately, current systems cannot provide safe a... ver más

30/04/2025

KTH

306K€

Presupuesto del proyecto: 306K€

Líder del proyecto

KUNGLIGA TEKNISKA HOEGSKOLAN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2025-04-30

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2024 ExpectedOutcome:

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3 Participantes

KTH

305.93K€ | Lider

TRUSTEES OF PRINCETON UNIVER...

PRINCETON

N.D. | Participante

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Duración del proyecto: 35 meses Fecha Inicio: 2022-05-02
Fecha Fin: 2025-04-30

Líder del proyecto

KUNGLIGA TEKNISKA HOEGSKOLAN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 306K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The FLASH project aims to establish the theoretical foundations of machine learning and wireless communications that will enable the vision of assisted and self-driving systems. Unfortunately, current systems cannot provide safe and reliable driving because they lack distributed and real-time learning algorithms meeting the critical latency and reliability requirements in highly dynamic and fast-varying wireless channels. Although the fifth generation of cellular systems supports the communication demands for assisted and self-driving, and machine learning proposes federated learning for distributed scenarios, the wireless communications and machine learning domains are not sufficiently integrated for real-time critical applications. Yet, it is only by their integration that the vision of assisted and self-driving will become real. To this end, we will establish a theoretical and algorithmic integration of federated learning and cellular networks that serve vehicles, which we refer to as federated learning supporting efficient and reliable inference over vehicular networks (FLASH). FLASH builds on the co-design of a fundamentally new ecosystem in which federated learning algorithms address critical constraints from vehicular applications, while resource allocation algorithms adapt wireless communication resources to the inference tasks. The project will implement FLASH by establishing and validating theoretical and algorithmic foundations for assisted and self-driving systems. Thus, we not only expect to have an academic impact but also a great societal impact by enabling the fulfilment of sustainable development goals through reduced fuel consumption, traffic emissions, and fatalities. Ultimately, the project provides outstanding training for a talented young researcher, Dr. Mairton Barros, at Princeton University for 24 months with Prof. H. Vincent Poor, and KTH Royal Institute of Technology for 12 months with Prof. Carlo Fischione.

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