Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks

Tang, Shunpu; Zhou, Wenqi; Chen, Lunyuan; Lai, Lijia; Xia, Junjuan; Fan, Liseng

doi:10.1016/j.phycom.2021.101381

Cited by 74 publications

(24 citation statements)

References 30 publications

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“…As a result, training models can be distributed while maintaining anonymity and lowering transmission costs. Because of these significant advantages, the use of FL in wireless technology is garnering increased research attention 13–15 . UAVs in IoT technology present several benefits and problems.…”

Section: Overview and Methodologymentioning

confidence: 99%

“…Because of these significant advantages, the use of FL in wireless technology is garnering increased research attention. [13][14][15] UAVs in IoT technology present several benefits and problems. There are significant worries regarding the authentication of UAVs and which UAVs enable them to fly due to their capabilities, such as quiet flight, shooting images and movies, and so forth.…”

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confidence: 99%

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Federated learning‐based IoT: A systematic literature review

Hosseinzadeh

Hemmati

Rahmani

2022

Int J Communication

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Summary The Internet of Things (IoT) has a significant impact on our daily lives as applications, services, devices, and industries become more intelligent. Artificial intelligence (AI) is expected to significantly influence machine learning training on IoT devices without data sharing. Federated learning (FL) is a distributed machine learning method used in many IoT smart devices; however, FL ensures IoT security and privacy. The systematic literature review (SLR) method is used in this paper to review recently published articles in the FL‐based IoT domain. We analyzed 39 papers that were published between 2018 and March 2022. According to the evaluation factors, the accuracy factor has a high percentage in the FL‐based IoT domain by 29%, and the epoch has 23%, the time has 18%, the energy consumption has 9%, the delay has 9%, the communication overhead has 6%, and the privacy has 6%. Finally, we discuss future research challenges and open issues in the context of FL‐based IoT.

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Section: Overview and Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

Federated learning‐based IoT: A systematic literature review

Hosseinzadeh

Hemmati

Rahmani

2022

Int J Communication

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“…In these works, which are outlined in Table 3, the execution of computation tasks is facilitated by the MEC nodes. To avoid raw data exchange in UAV swarms and protect data privacy, the Federated Edge Learning (FEEL) method was used in [92], in which the training data of a particular task is stored in a decentralized way across the UAVs in the swarm and the optimization problem is addressed cooperatively. However, the UAVs usually have batteries with a limited life, which may lead to an untimely dropping of these UAVs from FEEL training.…”

Section: Review Of Ml-inspired and Blockchain-based Security Solutionsmentioning

confidence: 99%

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

et al. 2022

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As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software-and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.INDEX TERMS Blockchain, Internet of Things (IoT), Machine Learning (ML), Mobile Edge Computing (MEC), Physical-Layer Security (PLS), Unmanned Aerial Vehicle (UAV).

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“…However, that work ignored latency and energy consumption in communication and computation. The authors in [49] presented a battery-constraint FL in drones with a centralized edge server. They tried to optimize the loss function in DRL for improving the latency and energy consumption.…”

Section: B Federated Learning For Resource Managementmentioning

confidence: 99%

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

et al. 2021

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Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be applied for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other's resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. The simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of -23% in energy consumption, -15% in latency, +18% in throughput, and +9% in fairness.INDEX TERMS Swarm of drones, Multi-access edge computing, Collaborative computing, Federated learning.

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Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks

Cited by 74 publications

References 30 publications

Federated learning‐based IoT: A systematic literature review

Federated learning‐based IoT: A systematic literature review

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

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