This paper investigates Deep Learning based Source Coding (DeepS C) with multiple users. While most of the existing works focus on a single pair of DeepSC transceivers, we consider multiple pairs of transceivers, each of which is modeled as a Vector Quantized Variational Autoencoder (VQ-VAE) architecture. Furthermore, in contrast to existing DeepSC works exploiting the trainability of encoders and decoders, in this work we focus on the trainability of codebooks. Inspired from this and Federated Learning (FL), we propose a novel DeepSC framework with federated codebook (FC- DeepSC) wherein each transceiver iteratively exchanges their codebooks during training, so as to construct an averaged global codebook that is downloaded by each transceiver. Simulation results corroborate that FC- DeepSC achieves faster convergence than DeepSC.
|Title of host publication||ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence|
|Subtitle of host publication||Accelerating Digital Transformation with ICT Innovation|
|Publisher||IEEE Computer Society|
|Number of pages||3|
|Publication status||Published - 2022|
|Event||13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of|
Duration: 2022 Oct 19 → 2022 Oct 21
|Name||International Conference on ICT Convergence|
|Conference||13th International Conference on Information and Communication Technology Convergence, ICTC 2022|
|Country/Territory||Korea, Republic of|
|Period||22/10/19 → 22/10/21|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2022R1A5A1027646).
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications