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SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks
[article]
2022
arXiv
pre-print
Preserved Fulltext
Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. ...
Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks. ...
On the contrary, federated learning improves the data privacy preservation, communication efficiency, and learning accuracy [1] . ...
arXiv:2211.11321v1
fatcat:mtskm7trb5aofavllyapngvlsq
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
2022
Sensors
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These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. ...
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. ...
Federated Learning and Blockchain for Privacy Preservation in Vehicular Networks Shortcomings of VANET are privacy, availability, integrity, identification, and confidentiality prevention from incoming ...
doi:10.3390/s22124394
pmid:35746176
pmcid:PMC9229631
fatcat:4p7klbusnndtdf2r2us6fp23xa
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
[article]
2023
arXiv
pre-print
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We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main ...
In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. ...
Dataset labeling: The labeling procedure is an integral part of data preparation for Supervised Split Federated Learning (SSFL). ...
arXiv:2309.09086v1
fatcat:ajbxt26v7vf4hgluf6dibjxole
Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges
2022
Journal of Cloud Computing: Advances, Systems and Applications
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On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning ...
Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still ...
[50] considered deploying federated learning in the vehicular networks and they proposed a new communication protocol, Fed-CPF. ...
doi:10.1186/s13677-022-00377-4
pmid:36536803
pmcid:PMC9753079
fatcat:mzio4vpiwzbzzldlggiowddtii
Federated Transfer Learning: concept and applications
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2010.15561v1 fatcat:jbs55vldrngjzfmxgcg6brvqaiOther Versions
We further analyze FTL from privacy and machine learning perspective. ...
Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. ...
The data federation as in case of FTL allows knowledge to be shared without compromising user privacy, and enables to be transferred in the network. ...
arXiv:2010.15561v3
fatcat:3udixrhta5btlb7w7r4fomwpzu
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
[article]
2020
arXiv
pre-print
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Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to ...
We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. ...
Acknowledgements This work is partly supported by the SEND project (grant ref.Âă32R16P00706) funded by ERDF and BEIS. ...
arXiv:2004.11794v2
fatcat:2cir7oiwyfevjfw7ymnnonbf5e
Split Learning in 6G Edge Networks
[article]
2024
arXiv
pre-print
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This leads to the emergence of split learning (SL) which enables servers to handle the major training workload while still enhancing data privacy. ...
In this article, we offer a brief overview of key advancements in SL and articulate its seamless integration with wireless edge networks. ...
Split Edge Learning with Label Privacy Preservation In conventional SL, labels should be placed on the server side. ...
arXiv:2306.12194v3
fatcat:bsdtfl5ldnh47bp3x32zbqy5iq
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles
[article]
2021
arXiv
pre-print
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To address this challenge, we propose a novel framework in this paper by leveraging the federated learning approaches for modelling energy consumption for BEVs (Fed-BEV). ...
We present the design of the proposed system architecture and implementation details in a co-simulation environment. ...
A global model based on a centralized federated learning method is trained in an iterative manner by leveraging each local model trained through local vehicular data collected from the first two steps. ...
arXiv:2108.04036v1
fatcat:n4nj3g6cs5d7jmg2xqy6hb6o3e
A Survey of Federated Learning for Edge Computing: Research Problems and Solutions
2021
High-Confidence Computing
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In this survey, we provide a new perspective on the applications, development tools, communication efficiency, security & privacy, migration and scheduling in edge federated learning. ...
Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. ...
Edge federated learning is a desirable solution in the VEC system to learn a privacy-preserving machine learning model from non-IID vehicular data [13] . ...
doi:10.1016/j.hcc.2021.100008
fatcat:fzzqredg6nfsxg6wlu5h7chixq
Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence
[article]
2021
arXiv
pre-print
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2022 accepted arXiv:2105.01798v2 fatcat:nqyjlsyvxncybnc23uccj7zaqy
We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. ...
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising ...
ACKNOWLEDGMENT This work was made possible by NPRP grant NPRP12S-0305-190231 and NPRP13S-0205-200265 from the Qatar National Research Fund (a member of Qatar Foundation). ...
arXiv:2105.01798v1
fatcat:4tnq2wjw4bcqdfvhnoij55s2rm
2020 Index IEEE Internet of Things Journal Vol. 7
2020
IEEE Internet of Things Journal
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., and Bose, R., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe ...
., +, JIoT April 2020 2553-2562 Privacy-Preserving Federated Learning in Fog Computing. ...
., +, JIoT Oct. 2020 10061-10071 + Check author entry for coauthors Privacy-Preserving Federated Learning in Fog Computing. ...
doi:10.1109/jiot.2020.3046055
fatcat:wpyblbhkrbcyxpnajhiz5pj74a
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
[article]
2020
arXiv
pre-print
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Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. ...
However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. ...
In the case of unsupervised learning, there is no data label. ...
arXiv:1909.11875v2
fatcat:a2yxlq672needkejenu4j3izyu
Rebirth of Distributed AI—A Review of eHealth Research
2021
Sensors
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Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. ...
Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. ...
Data Availability Statement: Not Applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21154999
fatcat:msnxvfkgwvatxhziel5x6d4l2a
Utilizing Federated Learning for Enhanced Real-Time Traffic Prediction in Smart Urban Environments
2024
International Journal of Advanced Computer Science and Applications
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Federated Learning (FL), a crucial advancement in smart city technology, combines real-time traffic predictions with the potential to enhance urban mobility. ...
This paper suggests a novel approach to real-time traffic prediction in smart cities: a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) architecture. ...
learning and data privacy preservation. ...
doi:10.14569/ijacsa.2024.0150267
fatcat:3bcu75geovebzbmnfmidjtnno4
Asynchronous Federated Learning on Heterogeneous Devices: A Survey
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2109.04269v4 fatcat:kf4pkwqwnbezvbliixbxeqxdqiOther Versions
With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. ...
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing ...
Similarly, in [103] , AFL is utilized to preserve data privacy and improve the quality of services in IIoT. ...
arXiv:2109.04269v5
fatcat:cbtfx2bu4zau3lild2y5tfyfsu
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