Projektbeschreibung
Umsetzung des Traums vom fahrerlosen Fahrzeug durch bessere verteilte algorithmische Integration
Föderales Lernen (FL) ist eine Technik des maschinellen Lernens (ML), bei der ein Algorithmus in Zusammenarbeit mit mehreren dezentralen Geräten geschult wird. Die Rechenleistung fahrerloser Fahrzeuge erfordert eine bessere Integration von FL in zelluläre Netze, um Reaktionen in Echtzeit zu ermöglichen. Das im Rahmen der Marie-Skłodowska-Curie-Maßnahmen finanzierte Projekt FLASH wird auf dieses Ziel hinarbeiten. Es wird ein Modell für FL-Algorithmen entwickeln, um die Einschränkungen verteilter Datensätze zu überwinden. Darüber hinaus werden Algorithmen entwickelt, mit denen die Ressourcen des Mobilfunknetzes den durch ML bestimmten Inferenzaufgaben zugewiesen werden können. Die Anwendung von FLASH-Ergebnissen in (teil-)autonomen Fahrzeugen wird nicht nur die Sicherheit und Zuverlässigkeit verbessern, sondern auch den Verbrauch fossiler Brennstoffe und Emissionen verringern.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsKoordinator
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