Autofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving

[PDF] arxiv.org

Vehicle as a service (VaaS): Leverage vehicles to build service networks and capabilities for smart cities

X Chen, Y Deng, H Ding, G Qu, H Zhang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …
被引用次数：14 相关文章 所有 3 个版本
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Efficient parallel split learning over resource-constrained wireless edge networks

Z Lin, G Zhu, Y Deng, X Chen, Y Gao… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
被引用次数：29 相关文章 所有 4 个版本
[PDF] arxiv.org

Formalizing multimedia recommendation through multimodal deep learning

D Malitesta, G Cornacchia, C Pomo, FA Merra… - arXiv preprint arXiv …, 2023 - arxiv.org
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in specific scenarios …
被引用次数：5 相关文章 所有 4 个版本
[PDF] arxiv.org

Satsense: Multi-satellite collaborative framework for spectrum sensing

H Yuan, Z Chen, Z Lin, J Peng, Z Fang, Y Zhong… - arXiv preprint arXiv …, 2024 - arxiv.org
Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service
with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

FedSN: A General Federated Learning Framework over LEO Satellite Networks

Z Lin, Z Chen, Z Fang, X Chen, X Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and
deployed successfully in space by commercial companies, such as SpaceX. Due to …
被引用次数：13 相关文章 所有 2 个版本
[PDF] arxiv.org

FedAC: A Adaptive Clustered Federated Learning Framework for Heterogeneous Data

Y Zhang, H Chen, Z Lin, Z Chen, J Zhao - arXiv preprint arXiv:2403.16460, 2024 - arxiv.org
Clustered federated learning (CFL) is proposed to mitigate the performance deterioration
stemming from data heterogeneity in federated learning (FL) by grouping similar clients for …
被引用次数：1 相关文章 所有 2 个版本

AutoSMC: An Automated Machine Learning Framework for Signal Modulation Classification

Y Wang, J Bai, Z Xiao, Z Chen, Y Xiong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The electromagnetic environments have become more complex with the development of
wireless communication technology. Signal modulation classification has attracted extensive …
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Federated Object Detection Scenarios for Intelligent Vehicles: Review, Case Studies, Experiments and Discussions

O Urmonov, S Sajid, Z Aziz… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …
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Balancing Similarity and Complementarity for Federated Learning

K Yan, S Cui, A Wuerkaixi, J Zhang, B Han… - arXiv preprint arXiv …, 2024 - arxiv.org
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively
using data while maintaining user privacy. One key challenge in FL is managing statistical …
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Robust multimodal federated learning for incomplete modalities

S Yu, J Wang, W Hussein, PCK Hung - Computer Communications, 2024 - Elsevier
Consumer electronics are continuously collecting multimodal data, such as audio, video,
and so on. A multimodal learning mechanism can be adopted to deal with these data. Due to …
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