期刊论文

Jia, ZW

英文

Remote Sensing

2072-4292

2023

15

14

3620-

10.3390/rs15143620

Conceptualization, Z.J. and H.Z.; methodology, Z.J. and H.Z.; software, H.Z.; validation, H.Z. and R.W.; formal analysis, H.Z.; investigation, W.Z.; resources, Z.J.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, W.Z.; visualization, R.W.; supervision, Z.J.; project administration, Z.J.; funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript. Z.J. and H.Z. contributed to the work equally and should be regarded as co-first authors. This research was supported by the National Natural Science Foundation of China (Grant No. 52207074), partly funded by the Research Foundation of Education Bureau of Hunan Province, China (Grant No.19B016), and the Postgraduate Scientific Research Innovation Project of Hunan Province, China (Grant No. CX20210796, CX20220919).

本校为第一且通讯机构

In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggreg...