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Journal Article
Design of Block Codes for Distributed Learning in VR/AR Transmission
- Authors
- Seo-Hee Hwang, Si-Yeon Pak, Jin-Ho Chung, Daehwan Kim, Yongwan Kim
- Issue Date
- 2023-12
- Citation
- Journal of Information and Communication Convergence Engineering, v.21, no.4, pp.300-305
- ISSN
- 2234-8255
- Publisher
- 한국정보통신학회
- Language
- English
- Type
- Journal Article
- Abstract
- Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.
- KSP Keywords
- Block codes, Complete information, Federated learning, Hamming weight, Learning effect, Security and efficiency, Unknown parameters, combined methods, distributed learning, information security, learning algorithm
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
