Data-Free Knowledge Distillation for Privacy-Preserving Efficient UAV Networks

Yu, Guyang

doi:10.1109/icras55217.2022.9842039

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…To prevent direct contact with private information, TS transfers the complex neural network to lightweight networks with the consideration of preserving privacy. The new lightweight network is deployed in UAV networking to assist UAVs in recognizing targets [31].…”

Section: Knowledge Distillationmentioning

confidence: 99%

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

Ren,

Restivo,

Tan

et al. 2023

Future Internet

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As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber–physical systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead a UAV to fly into a sensitive area and threaten public safety and private security. The conventional spoofing detection methods need too much overhead, which stops efficient detection from working in a computation-constrained UAV and provides an efficient response to attacks. In this paper, we propose a novel approach to obtain a lightweight detection model in the UAV system so that GPS spoofing attacks can be detected from a long distance. With long-short term memory (LSTM), we propose a lightweight detection model on the ground control stations, and then we distill it into a compact size that is able to run in the control system of the UAV with knowledge distillation. The experimental results show that our lightweight detection algorithm runs in UAV systems reliably and can achieve good performance in GPS spoofing detection.

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Section: Knowledge Distillationmentioning

confidence: 99%

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

Ren,

Restivo,

Tan

et al. 2023

Future Internet

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“…Several works have already used KD to obtain lightweight models suitable for UAVs. For example, Li et al [16] have applied this technique for video saliency estimation, while Liu et al [17], Yu [18], Ding et al [19], and Luo et al [20] used it for object detection, object recognition, action recognition, and UAV delivery, respectively. However, to our knowledge, no work has investigated knowledge distillation to produce efficient and accurate models tailored for UAVs in the context of weed mapping.…”

Section: B Knowledge Distillationmentioning

confidence: 99%

Applying Knowledge Distillation to Improve Weed Mapping With Drones

Castellano,

Marinis,

Vessio

2023

Annals of Computer Science and Information Systems

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In precision agriculture, non-invasive remote sensing using UAVs can be employed to observe crops in visible and nonvisible spectra. This paper investigates the effectiveness of stateof-the-art knowledge distillation techniques for mapping weeds with drones, an essential component of precision agriculture that employs remote sensing to monitor crops and weeds. The study introduces a lightweight Vision Transformer-based model that achieves optimal weed mapping capabilities while maintaining minimal computation time. The research shows that the student model effectively learns from the teacher model using the WeedMap dataset, achieving accurate results suitable for mobile platforms such as drones, with only 0.5 GMacs compared to 42.5 GMacs of the teacher model. The trained models obtained an F1 score of 0.863 and 0.631 on two data subsets, with a performance improvement of 2 and 7 points, respectively, over the undistilled model. The study results suggest that developing efficient computer vision algorithms on drones can significantly improve agricultural management practices, leading to greater profitability and environmental sustainability.

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“…One of the most promising approaches for model compression and domain adaptation is Knowledge Distillation (KD) [6], a machine learning paradigm that relies on the transfer of knowledge from a large Teacher network to a less complex Student model that can be implemented on the edge. In other application domains, KD has demonstrated effective results in maintaining the performance of the Teacher network, while facilitating scalability [7] and preservation of privacy [8].…”

Section: Introductionmentioning

confidence: 99%

Improving Knowledge Distillation for Non-Intrusive Load Monitoring Through Explainability Guided Learning

Batic

Tanoni

Stanković

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Knowledge distillation (KD) is a machine learning technique widely used in recent years for the task of domain adaptation and complexity reduction. It relies on a Student-Teacher mechanism to transfer the knowledge of a large and complex Teacher network into a smaller Student model. Given the inherent complexity of large Deep Neural Network (DNN) models, and the need for deployment on edge devices with limited resources, complexity reduction techniques have become a hot topic in the Non-intrusive Load Monitoring (NILM) community. Recent literature in NILM has devoted increased effort to domain adaptation and architecture reduction via KD. However, the mechanism behind the transfer of knowledge from the Teacher to the Student is not clearly understood. In this work, we aim to address the aforementioned issue by placing the KD NILM approach in a framework of explainable AI (XAI). We identify the main inconsistency in the transfer of explainable knowledge, and exploit this information to propose a method for improvement of KD through explainability guided learning. We evaluate our approach on a variety of appliances and domain adaptation scenarios and demonstrate that solving inconsistencies in the transfer of explainable knowledge can lead to improvement in predictive performance.

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Data-Free Knowledge Distillation for Privacy-Preserving Efficient UAV Networks

Cited by 5 publications

References 13 publications

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

Applying Knowledge Distillation to Improve Weed Mapping With Drones

Improving Knowledge Distillation for Non-Intrusive Load Monitoring Through Explainability Guided Learning

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