Descripción del proyecto
Cumplir el sueño de los vehículos sin conductor mediante una mejor integración de algoritmos distribuidos
El aprendizaje federado (AF) es una técnica de aprendizaje automático (AA) que entrena de forma colaborativa un algoritmo en múltiples dispositivos descentralizados. Las exigencias computacionales de los vehículos sin conductor requieren una mejor integración del AF con las redes celulares para obtener respuestas al instante. El equipo del proyecto FLASH, financiado por las Acciones Marie Skłodowska-Curie, trabajará con este objetivo. Creará un modelo para que los algoritmos de AF gestionen las limitaciones de los conjuntos de datos distribuidos. También desarrollará algoritmos capaces de asignar los recursos de la red celular a las tareas de inferencia determinadas por el AA. La aplicación de los resultados de FLASH en los vehículos (semi)autónomos mejorará la seguridad y la fiabilidad, además de reducir el uso de combustibles fósiles y las emisiones.
Objetivo
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.
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network
- natural sciencescomputer and information sciencescomputer securitynetwork security
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinador
100 44 Stockholm
Suecia