BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Peng, Yongqiang; Chen, Zongyao; Chen, Zexuan; Ou, Wei; Han, Wenbao; Ma, Jianqiang

doi:10.1155/2021/6633332

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…The authors in [180] choose distinct FL methods according to the distinct distribution characteristics of the data source, and adopts a lightweight encryption algorithm CPC to protect privacy, in comparison with other symmetric encryption algorithms, which eliminates the expense of computation. In [181], the authors design encryption algorithms for two distinct MEC servers.…”

Section: High Information Sensitivitymentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Section: High Information Sensitivitymentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Furthermore, adaptive FL frameworks have been proposed to cater to the unique requirements of autonomous vehicles. Peng et al ( 2021 ) introduced an adaptive FL framework for autonomous vehicles, taking into account dynamic network conditions and resource constraints. Similarly, in Zhang et al ( 2021c ), the authors addressed the problem of distributed dynamic map fusion using FL techniques to facilitate collaboration among intelligent networked vehicles.…”

Section: Related Workmentioning

confidence: 99%

Res-FLNet: human-robot interaction and collaboration for multi-modal sensing robot autonomous driving tasks based on learning control algorithm

Wang

2023

Front. Neurorobot.

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IntroductionRes-FLNet presents a cutting-edge solution for addressing autonomous driving tasks in the context of multimodal sensing robots while ensuring privacy protection through Federated Learning (FL). The rapid advancement of autonomous vehicles and robotics has escalated the need for efficient and safe navigation algorithms that also support Human-Robot Interaction and Collaboration. However, the integration of data from diverse sensors like cameras, LiDARs, and radars raises concerns about privacy and data security.MethodsIn this paper, we introduce Res-FLNet, which harnesses the power of ResNet-50 and LSTM models to achieve robust and privacy-preserving autonomous driving. The ResNet-50 model effectively extracts features from visual input, while LSTM captures sequential dependencies in the multimodal data, enabling more sophisticated learning control algorithms. To tackle privacy issues, we employ Federated Learning, enabling model training to be conducted locally on individual robots without sharing raw data. By aggregating model updates from different robots, the central server learns from collective knowledge while preserving data privacy. Res-FLNet can also facilitate Human-Robot Interaction and Collaboration as it allows robots to share knowledge while preserving privacy.Results and discussionOur experiments demonstrate the efficacy and privacy preservation of Res-FLNet across four widely-used autonomous driving datasets: KITTI, Waymo Open Dataset, ApolloScape, and BDD100K. Res-FLNet outperforms state-of-the-art methods in terms of accuracy, robustness, and privacy preservation. Moreover, it exhibits promising adaptability and generalization across various autonomous driving scenarios, showcasing its potential for multi-modal sensing robots in complex and dynamic environments.

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“…That's why Federated Transfer Learning came out. Federated Transfer Learning is the expand of Federated Learning, which can be used on not only two sample spaces, but also two different datasets [6].…”

Section: Federated Transfer Learningmentioning

confidence: 99%

Federated transfer learning: Concept and application

2023

ACE

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As the development of Machine Learning, more and more data are used to train models in order to guide peoples daily life. Faced with privacy and efficiency challenges these days, Federated Transfer Learning (FTL) has grown in popularity, as it has a great ability to protect data privacy while also dealing with the problem of data scarcity. In this passage, the author studies the FTL and its different applications, including sales industry, Industrial Internet of Things Devices, Finance application, Medical Application and Autonomous Driving, analyses the usage in the different industries. Further, the author discusses the privacy and robustness of FTL, which are the core features of FTL. In the end, the author concluded the features of FTL and also gave a prospection of FTL.

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BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Cited by 17 publications

References 29 publications

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Res-FLNet: human-robot interaction and collaboration for multi-modal sensing robot autonomous driving tasks based on learning control algorithm

Federated transfer learning: Concept and application

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