Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Zhu, Chaoyang; Zhu, Xiaoyan; Ren, Junyu; Qin, Tuanfa

doi:10.1109/access.2022.3174865

Cited by 29 publications

(16 citation statements)

References 107 publications

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“…Other studies focus on the integration of blockchain and FL in ITS. Zhu et al [41] discuss UAV edge network architectures that support blockchain and FL. Javed et al [42] discuss computational cost, communication overhead, and privacy issues in ITS, which can be addressed through the integration of blockchain and FL.…”

Section: A Current State Of Art and Our Contributionsmentioning

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: A Current State Of Art and Our Contributionsmentioning

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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“…A federated learning (FL) and blockchain technology have been used in Zhu et al 27 to secure unmanned aerial vehicles (UAVs). The FL is a decentralized distributed architecture that supports user data privacy, while blockchain supports anonymization, immutable, and distributed features.…”

Section: Background and Related Workmentioning

confidence: 99%

A blockchain‐enabled security management framework for mobile edge computing

Alzubi,

Singh

et al. 2023

Int J Network Mgmt

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Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet‐based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain‐enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain‐enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource‐constrained MEC devices. In addition, a smart contract‐based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.

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“…For privacy and high-level security, the authors of [26]introduced blockchains for securing data sharing in B5G for UAV computing networks. Reference [27] highlighted blockchain-enabled FL in UAV edge computing networks. Furthermore, the authors of [28] discussed the combination of blockchain and FL for UAV edge intelligence in smart environments.…”

Section: Related Workmentioning

confidence: 99%

“…[21] Air learning for autonomous UAVs × × × (2021) [22] FL for scheduling and power allocation cooperation of UAV swarms × × × (2020) [24] Challenges and architecture of decentralized FL in UAV networks × × × (2021) [26] Securing data sharing for UAV computing networks × × × × (2021) [27] Performance of FL for edge-assisted UAV networks with data sharing × × × (2020) [29] UAV-enabled edge computing to support IoT devices × × (2021) [28] Combination of FL and Blockchain for drone edge intelligence over × × (2021) smart environments…”

Section: Ref Highlight [A] [B] [C] [D] [E]mentioning

confidence: 99%

UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

147

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Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

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Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Cited by 29 publications

References 107 publications

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

A blockchain‐enabled security management framework for mobile edge computing

UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation

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