Digital immersion via virtual reality (VR) has promising applications in entertainment, education, and business. However, VR transmissions over wireless are data-intensive and computation-intensive. It is critical to investigate novel wireless network solutions that meet stringent quality-of-service requirements in VR. In this paper, we propose a novel VR transmission scheme to transmit single-view images only. Particularly, single-view images are broadcasted to users with the overlapped field of view, and the corresponding multi-view consistent content is generated by a neural network to avoid massive content transmission. We design a federated learning framework to guarantee an efficient learning process by characterizing vertical and horizontal data samples. Meanwhile, exchanging parts of models during the federated learning process can achieve low-latency communications. Simulation results validate the effectiveness of the proposed VR transmission scheme.


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    Titel :

    Federated Learning for Multi-view Synthesizing in Wireless Virtual Reality Networks


    Beteiligte:
    Guo, Yiyu (Autor:in) / Qin, Zhijin (Autor:in)


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    1085590 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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