End-to-End Distortion Modeling for Error-Resilient Screen Content Video Coding

Tang, Tong; Yin, Zhiyang; Li, Jie; Wang, Honggang; Wu, Dapeng; Wang, Ruyan

doi:10.1109/tmm.2023.3323895

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…However, IoV device data are usually unique to the holder, which hinders the development process of ITS. The characteristic of distributed training of federated learning [3,4] can effectively solve this problem by aggregating and updating multi-party parameters, achieving data sharing and joint model training. Recently, emerging technologies such as wireless charging, e.g., [5,6], have been able to ensure increased device uptime and broader participation in federated learning.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning

Zhou,

Jiang,

Tang

2024

WEVJ

Self Cite

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Due to the wide connection range and open communication environment of internet of vehicle (IoV) devices, they are susceptible to Byzantine attacks and privacy inference attacks, resulting in security and privacy issues in IoV federated learning. Therefore, there is an urgent need to study IoV federated learning methods with privacy protection. However, the heterogeneity and resource limitations of IoV devices pose significant challenges to the aggregation of federated learning model parameters. Therefore, this paper proposes an asynchronous robust aggregation method with privacy protection for federated learning in IoVs. Firstly, we design an asynchronous grouping robust aggregation algorithm based on delay perception, combines intra-group truth estimation with inter-group delay aggregation, and alleviates the impact of stragglers and Byzantine attackers. Then, we design a communication-efficient and security enhanced aggregation protocol based on homomorphic encryption, to achieve asynchronous group robust aggregation while protecting data privacy and reducing communication overhead. Finally, the simulation results indicate that the proposed scheme could achieve a maximum improvement of 41.6% in model accuracy compared to the baseline, which effectively enhances the training efficiency of the model while providing resistance to Byzantine attacks and privacy inference attacks.

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Section: Introductionmentioning

confidence: 99%

Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning

Zhou,

Jiang,

Tang

2024

WEVJ

Self Cite

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Popularity Balanced Multi-Task Bundling for Mobile Crowd Sensing

Zhen,

Wang,

et al. 2024

IEEE Trans. Knowl. Data Eng.

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SHN: rock image classification and feature visualization using multiple granularity spatial disorder hierarchical residual network

Zhang,

Liu,

Guo

et al. 2024

Front. Earth Sci.

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The automated classification of rock images is of paramount importance in geological analysis, as it serves as the foundational criterion for the categorization of rock lithology. Despite recent advancements in leveraging deep learning technologies to enhance the efficiency and precision of image classification, a crucial aspect has been overlooked: these methods face a performance bottleneck when attempting to apply it directly to rock classification methods. To address this limitation, we propose a multiple granularity Spatial disorder Hierarchical residual Network (SHN). This approach involves learning from objects annotated at different levels, thereby facilitating the transfer of hierarchical knowledge across levels. By enabling lower-level classes to inherit pertinent attributes from higher-level superclasses, our method aims to capture the intricate hierarchical relationships among different rock types. Especially, we introduce a multi-granularity spatial disorder module to aid neural networks in discerning discriminative details across various scales. This module enables processed images to exhibit region independence, compelling the network to adeptly identify discriminative local regions at diverse granularity levels and extract pertinent features. Furthermore, in light of the absence of a comprehensive rock dataset, this study amassed 4,227 rock images of diverse compositions from various places, culminating in the creation of a robust rock dataset for classification. Rigorous experimentation on this dataset yielded highly promising results, demonstrating the effectiveness of our proposed method in addressing the challenges of rock image classification.

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End-to-End Distortion Modeling for Error-Resilient Screen Content Video Coding

Cited by 4 publications

References 38 publications

Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning

Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning

Popularity Balanced Multi-Task Bundling for Mobile Crowd Sensing

SHN: rock image classification and feature visualization using multiple granularity spatial disorder hierarchical residual network

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