Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Wang, Yuntao; Su, Zhou; Zhang, Ning; Benslimane, Abderrahim

doi:10.1109/tnse.2020.3014385

Cited by 195 publications

(94 citation statements)

References 51 publications

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“…Even for UAV-based networks, the research community has recently begun to apply the concept of a Blockchain coupled to FL to propose solutions for UAV networks. For example, the authors in [187] proposed a secure FL framework for a mobile crowdsensing application assisted by a UAV-network. The local exchanges of the FL algorithm were secured on the basis of a Blockchain architecture.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

110

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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

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Section: Discussion and Future Workmentioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

110

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“…In this way, each IoT node only needs to compute the model based on its mini-batch instead of the whole datasets, which will reduce computation latency accordingly. Recently, FL is also employed to build a secure UAV-based crowdsensing approach [100], as shown in Fig. 8.…”

Section: E Fl For Iot Mobile Crowdsensingmentioning

confidence: 99%

“…In this way, each IoT node only needs to compute the model based on its mini-batch instead of the whole datasets, which will reduce computation latency accordingly. Among decentralized technologies, blockchain is a strong candidate that can be combined with FL to decentralize the learning process in UAV-based crowdsensing services [100]. FL allows UAVs to train local models using sensing datasets and share updates via the blockchain ledger for server communication and model combination in a secure and transparent manner.…”

Section: Cloud Servermentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

684

182

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“…In [11], the humblebees are equipped with delicate micro sensors to perform sensing operation. In [12], the researchers proposed a secure federated learning framework for UAV-assisted mobile crowd sensing to collect data. In [13], the researchers explored data collection in the IoT.…”

Section: A Data Collection In Agriculturementioning

confidence: 99%

Privacy Preserved Cyber-Physical Searching for Information-Centric Intelligent Agriculture

Ding

Zhang³

et al. 2021

IEEE Open J. Comput. Soc.

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Technological evolution has brought great changes to traditional industries. Nowadays farmers obtain great benefits such as intelligent production and convenient management from intelligent agriculture. Nevertheless, there still exists many flaws in current solutions. Deficiencies in energy management, data transmission and privacy preservation are supposed to be solved. In this paper, the privacy preserved cyber-physical searching framework is proposed to tackle these problems. This four-layer framework can provide a convenient way for farmers to manage production by simply searching for information they interested in. The data source layer contains many sensors for data collection. The data aggregation layer consists of data aggregation nodes deployed in the farms. The data transfer layer employs drones for retrieving data from data aggregation nodes. The cloud control layer is responsible for searching requests processing and privacy preservation. To realize better data collection and searching. The information centric network is introduced into this framework. Moreover, some methods are employed for privacy preservation. The simulation results show the feasibility of this framework.

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Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Cited by 195 publications

References 51 publications

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Privacy Preserved Cyber-Physical Searching for Information-Centric Intelligent Agriculture

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