Systems and methods for vehicular-network-assisted federated machine learning are disclosed herein. One embodiment transmits first metadata from a connected vehicle to at least one other connected vehicle; receives, at the connected vehicle, second metadata from the at least one other connected vehicle; receives, at the connected vehicle based on analysis of the first and second metadata, a notification that the connected vehicle has been elected to participate in the current training phase of a federated machine learning process; receives, at the connected vehicle, instructions to prepare the connected vehicle for the next training phase; trains a machine learning model to perform a task at the connected vehicle during the current training phase to produce a locally trained machine learning model; and submits the locally trained machine learning model for aggregation with at least one other locally trained machine learning model to produce an aggregated locally trained machine learning model.


    Access

    Download


    Export, share and cite



    Title :

    SYSTEMS AND METHODS FOR VEHICULAR-NETWORK-ASSISTED FEDERATED MACHINE LEARNING


    Contributors:

    Publication date :

    2022-08-11


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G01C Messen von Entfernungen, Höhen, Neigungen oder Richtungen , MEASURING DISTANCES, LEVELS OR BEARINGS / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / H04W WIRELESS COMMUNICATION NETWORKS , Drahtlose Kommunikationsnetze



    Clustered Vehicular Federated Learning: Process and Optimization

    Taik, Afaf / Mlika, Zoubeir / Cherkaoui, Soumaya | IEEE | 2022


    FedAGL: A Communication-Efficient Federated Vehicular Network

    Liu, Su / Li, Yushuai / Guan, Peiyuan et al. | IEEE | 2024


    Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

    ZHAO, Hongbo / GENG, Liwei / FENG, Wenquan et al. | Elsevier | 2024

    Free access

    Federated Learning for Anomaly Detection in Vehicular Networks

    Tham, Chen-Khong / Yang, Lu / Khanna, Akshit et al. | IEEE | 2023


    Energy-Aware Blockchain and Federated Learning-Supported Vehicular Networks

    Aloqaily, Moayad / Ridhawi, Ismaeel Al / Guizani, Mohsen | IEEE | 2022