Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164442
Title: Robust semi-supervised federated learning for images automatic recognition in internet of drones
Authors: Zhang, Zhe
Ma, Shiyao
Yang, Zhaohui
Xiong, Zehui
Kang, Jiawen
Wu, Yi
Zhang, Kejia
Niyato, Dusit
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Zhang, Z., Ma, S., Yang, Z., Xiong, Z., Kang, J., Wu, Y., Zhang, K. & Niyato, D. (2022). Robust semi-supervised federated learning for images automatic recognition in internet of drones. IEEE Internet of Things Journal. https://dx.doi.org/10.1109/JIOT.2022.3151945
Journal: IEEE Internet of Things Journal
Abstract: Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose model parameters mixing strategy to improve the naive combination of FL and semi-supervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as Federated Mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-IID levels of client data.
URI: https://hdl.handle.net/10356/164442
ISSN: 2327-4662
DOI: 10.1109/JIOT.2022.3151945
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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