DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 강준혁 | - |
dc.contributor.author | Lee, Jongmyeong | - |
dc.contributor.author | 이종명 | - |
dc.date.accessioned | 2024-07-25T19:31:17Z | - |
dc.date.available | 2024-07-25T19:31:17Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045923&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320692 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.8,[ii, 34 p. :] | - |
dc.description.abstract | The collaborative machine learning technique known as Federated Learning (FL) has been the subject of active research, allowing multiple devices to learn together. Recent research has focused on model-heterogeneous FL, which aims to address the computational differences between participating devices. However, these studies have assumed an ideal communication channel without noise, which is not reflective of real-world wireless communication environments. This paper presents an evaluation of the performance of benchmark algorithms FjORD and HeteroFL in a wireless communication environment. Additionally, we propose a more communication-efficient algorithm, W-FjORD, for model-heterogeneous FL. Numerical results indicate that the proposed scheme significantly improves performance compared to benchmark schemes. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 연합 학습▼a무선 통신▼a모델-이질적인▼a기계 학습▼a정확도 | - |
dc.subject | Federated learning (FL)▼aWireless communication▼aModel-heterogeneous▼aMachine learning▼aAccuracy | - |
dc.title | Model-heterogeneous federated learning in wireless networks | - |
dc.title.alternative | 무선 통신 환경에서의 모델 이질적인 연합학습 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Kang, Joonhyuk | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.