Current Result Document :
| 한글제목(Korean Title) |
OctoFedS: 엣지에서 객체 검출을 위한 스플릿 컴퓨팅 연합 학습 시스템 |
| 영문제목(English Title) |
OctoFedS: A Federated Split Learning System for Object Detection at the Edge |
| 저자(Author) |
Bich-Ngoc Doan
Thanh-Tung Nguyen
이동만
Bich-Ngoc Doan
Thanh-Tung Nguyen
Dongman Lee
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| 원문수록처(Citation) |
VOL 49 NO. 01 PP. 1934 ~ 1936 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The large amount of data generated continuously by user devices at the Edge of the network can be leveraged to further improve state-of-the-art deep learning models. However, this practice presents new challenges in terms of data privacy. In this paper, we design OctoFedS, a federated split learning system that adopted Federated Learning and Split Computing to train object detection models without exposing data to privacy threats. Our experiments with model YOLOv1 using the PascalVOC dataset in distributed settings proved the validity of our approach while still achieving mean Average Precision of 0.66 (mAP). |
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| 파일첨부 |
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