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- W4317350114 abstract "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." @default.
- W4317350114 created "2023-01-19" @default.
- W4317350114 creator A5010632517 @default.
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- W4317350114 date "2022-09-01" @default.
- W4317350114 modified "2023-09-26" @default.
- W4317350114 title "Federated Learning for Multi-view Synthesizing in Wireless Virtual Reality Networks" @default.
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- W4317350114 doi "https://doi.org/10.1109/vtc2022-fall57202.2022.10012859" @default.
- W4317350114 hasPublicationYear "2022" @default.
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