CIOFL: Collaborative Inference- Based Online Federated Learning for UAV Object Detection

被引：1

作者：

Wu, Feiyu ^{[1
]}

Dong, Chao ^{[1
]}

Qu, Yuben ^{[1
]}

Sun, Hao ^{[1
]}

Zhang, Lei ^{[1
]}

Wu, Qihui ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Minist Ind & Informat Technol, Key Lab Dynam Cognit Syst Electromagnet Spectrum, Nanjing, Peoples R China

来源：

2022 IEEE 19TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2022) | 2022年

基金：

中国国家自然科学基金;

关键词：

federated learning; collaborative inference; online learning; UAV; object detection;

D O I：

10.1109/MASS56207.2022.00043

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Federated learning (FL) has great potential in visual applications such as object detection by unmanned aerial vehicles (UAVs), since different UAVs can capture diverse characteristics of targeted objects from different angles. More importantly, how to collaboratively train an accurate object detection model via FL in an online manner is critical to UAV object detection in practice. In this demonstration, we present a working prototype of CIOFL on embedded computers, Qollaborative Inference-based Qnline Eederated Learning for object detection within a UAV swarm. In essence, the proposed CIOFL enables online FL for object detection among multiple nodes, by continuously adding high-quality real-world samples inferred just by these nodes with a complex and large-scale object detection model. Our evaluation results show that, compared to the traditional FL, CIOFL improves the convergence rate and accuracy by similar to 1.3 x and similar to 1.34 x, respectively. We envision that, the CIOFL can effectively enhance the applicability of UAVs conducting object detection in practice

引用

页码：258 / 259

页数：2