FedSnM: P2P 네트워크에서 효율적인 통신을 위한Score-and-Model 방식을 활용한 연합학습

FedSnM: Score-and-Model based Communication-Efficient Federated Learning in Peer-to-Peer Network Environment

초록

A digital twin is a technology that creates a virtual world identical to the real world. Problems in the real world can be identified through various simulations, so it is a trend to be applied in various industries. In order to apply the digital twin, it is necessary to analyze the drawings in which the structure of the real world to be made identical is designed. Although the technology for analyzing drawings is being studied, it is difficult to apply them because the rules or standards for drawing drawings are different for each author. Therefore, in this paper, we implement a system that analyzes and classifies the vertex detail, one of the drawings, using artificial intelligence. Through this, we intend to confirm the possibility of analyzing and classifying drawings through artificial intelligence and introduce future research directions.

키워드

Federated Learning; Decentralized Learning; Gossip Protocol; Particle Swarm Optimization; 디지털 트윈; 격점상세도; 퓨샷 러닝; 이미지 분류
제목
FedSnM: P2P 네트워크에서 효율적인 통신을 위한Score-and-Model 방식을 활용한 연합학습
제목 (타언어)
FedSnM: Score-and-Model based Communication-Efficient Federated Learning in Peer-to-Peer Network Environment
저자
박성환; 이재우
DOI
10.6109/jkiice.2023.27.2.192
발행일
2023-02
저널명
한국정보통신학회논문지
권
27
호
2
페이지
1809 ~ 1815