| Title |
A Survey on Distributed Machine Learning-empowered Unmanned Aerial Vehicles |
| Authors |
이준영(Junyoung Lee) ; 이주형(Joohyung Lee) |
| DOI |
https://doi.org/10.5573/ieie.2024.61.3.14 |
| Keywords |
Federated learning; Split learning; Decentralized federated learning; Unmanned aerial vehicle networks |
| Abstract |
Unmanned Aerial Vehicles (UAVs) are attracting attention as a core technology for 6G networks. UAVs can provide a variety of advanced application programs by utilizing Machine Learning (ML) algorithms. In addition, machine learning can be used to automate the operation of UAVs. However, due to the increase in data transmission and security concerns for centralized machine learning, distributed machine learning technology is being studied rapidly. In particular, studies on various application methods in consideration of UAVs' limited computing and wireless network resources and battery performance were conducted. In this paper, we cover extensively the latest research on UAV networks that utilize federated learning, Split learning, and Decentralized Federated Learning among distributed machine learning. First, we introduce learning scenarios for each distributed machine learning method, and then analyze the latest related research on UAV based on distributed machine learning to describe the main technical characteristics and introduce technical issues and challenges. |