Federated Learning Based on CTC for Heterogeneous Internet of Things

Gao, Demin; Wang, Haoyu; Guo, Xiaowei; Wang, Lei; Gui, Guan; Wang, Weizheng; Yin, Zhimeng; Wang, Shuai; Liu, Yunhuai; He, Tian

doi:10.1109/jiot.2023.3305189

Cited by 23 publications

(6 citation statements)

References 63 publications

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“…Pseudo-label learning involves incorporating unlabeled data into model training to enhance the performance of supervised processes by utilizing model-predicted transformed hard labels [32][33][34][35]. Occasionally, certain samples may exhibit uncertainty regarding their categorization across multiple classes.…”

Section: Pseudo-label Learningmentioning

confidence: 99%

Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Gao,

Cai,

et al. 2023

Electronics

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Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliable global–local representation learning and dynamic label weighting. Our framework incorporates two key modules: the Pseudo-Complementary Label Generation (PCLG) module, which leverages pseudo-labels and complementary labels obtained using a credibility threshold to learn domain-invariant global and local features, and the Label Dynamic Weight Matching (LDWM) module, which assesses the learning difficulty of each category and adaptively assigns corresponding label weights, thereby enhancing the classification performance in the target domain. We evaluate our approach through extensive experiments and analyses on multiple public datasets, including RAF-DB, FER2013, CK+, JAFFE, SFW2.0, and ExpW. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, with an average accuracy improvement of 3.5% across the five datasets.

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Section: Pseudo-label Learningmentioning

confidence: 99%

Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Gao,

Cai,

et al. 2023

Electronics

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“…Alfadhli et al [38] proposed a lightweight multifactor authentication scheme that preserves privacy and combines PUF and multifactor authentication to support V2V interaction without sharing sensitive privacy data with RSUs. In the dynamic vehicle scenario, Chai et al [39] introduced a hierarchical weighted update federated learning [40] algorithm to facilitate knowledge sharing. Various chains are tasked with recording diverse environmental data to enable the federated learning process, thereby facilitating knowledge sharing.…”

Section: Blockchain Technology Application Research In the Iovmentioning

confidence: 99%

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Liu,

Qian,

Zhang

et al. 2023

Sustainability

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With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.

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“…The professionals must obtain precise target feature data from remote sensing images through direct assessment or by utilizing auxiliary decoding instruments. In recent years, rapid advancements in remote sensing and sensor technologies have yielded a wealth of abundant and precise multispectral and hyper-spectral images, enabling vegetation classification based on remote sensing [12][13][14] data. Hyperspectral remote sensing employs imaging spectrometers to capture high-resolution spectral image data across various regions of the electromagnetic spectrum, including the visible, near-infrared, mid-infrared, and thermal-infrared bands, within a narrow and continuous wavelength range [15].…”

Section: Introductionmentioning

confidence: 99%

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Xu,

Chen,

Xie

et al. 2024

Fire

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Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires.

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Federated Learning Based on CTC for Heterogeneous Internet of Things

Cited by 23 publications

References 63 publications

Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

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