Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu, R. Y.; Li, Mengyao; Yin, Jiangjin; Sun, Lihui; Líu, Hao

doi:10.1109/tits.2023.3243088

Cited by 6 publications

(2 citation statements)

References 202 publications

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“…Authors in [16] focused on connected and automated vehicles, exploring how Federated Learning (FL) can enhance communication and collaboration among vehicles, contributing to the development of intelligent vehicular systems. Their survey paper discusses existing approaches and challenges in applying FL [17]. Badidi et al investigated the synergies between FL and Edge Computing, emphasizing their collaborative potential.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

J.J.Jayakanth

2024

jes

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This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy concerns associated with centralized approaches. Current challenges in real-time processing, privacy preservation, and adaptability to dynamic environments are addressed through innovative solutions. Model optimization techniques optimize TCN efficiency, ensuring real-time processing. Advanced privacy-preserving mechanisms secure FL, addressing privacy concerns. Transfer learning and data augmentation enhance adaptability to dynamic scenarios. The proposed system not only addresses existing challenges but also contributes to the evolution of surveillance technology. Comprehensive metrics, including accuracy, sensitivity, specificity, and real-time processing metrics, provide a thorough evaluation of the system's performance. This research introduces an approach to dynamic object detection, combining TCN and FL in edge computing environments. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 98%, and F1 score reaching 96%.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

J.J.Jayakanth

2024

jes

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“…Hence, the local computation latency of the tth FL round is given by max u T u t , as the server has to wait for the slowest user. This limits FL applications with tight latency constraints, e.g., intelligent transportation systems [37], as illustrated in Fig. 1.…”

Section: B System Heterogeneitymentioning

confidence: 99%

Joint Privacy Enhancement and Quantization in Federated Learning

Lang

Shlezinger

2022

2022 IEEE International Symposium on Information Theory (ISIT)

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Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets, and the communication load due to the repeated exchange of updated models. These challenges are often tackled individually via techniques that induce some distortion on the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compression. In this work we propose a method coined joint privacy enhancement and quantization (JoPEQ), which jointly implements lossy compression and privacy enhancement in FL settings. In particular, JoPEQ utilizes vector quantization based on random lattice, a universal compression technique whose byproduct distortion is statistically equivalent to additive noise. This distortion is leveraged to enhance privacy by augmenting the model updates with dedicated multivariate privacy preserving noise. We show that JoPEQ simultaneously quantizes data according to a required bit-rate while holding a desired privacy level, without notably affecting the utility of the learned model. This is shown via analytical LDP guarantees, distortion and convergence bounds derivation, and numerical studies. Finally, we empirically assert that JoPEQ demolishes common attacks known to exploit privacy leakage. publicly available [24]. LDP can be boosted by corrupting the model updates with privacy preserving noise (PPN) [25], via splitting/shuffling [26] or dimension selection [27], and by exploiting the noise induced when communicating over a shared wireless channel [28], [29]. Prior works also studied the trade-offs between user privacy, utility, and transmission rate; providing utility [24] and convergence [30] bounds. Several recent studies consider both challenges of compression and privacy in FL. The works [31], [32] quantize the local gradient with a differentially private 1-bit compressor.That is, the probability of each coordinate of the gradients to be encoded into one of two possible dictionary words is designed to satisfy the Gaussian mechanism; thus the communication burden is reduced while differential privacy (DP) is simultaneously guaranteed. However, these methods utilize fixed 1-bit quantizers, and cannot be configured into adaptable communication budget once available. In [33], the authors combine privacy and compression by converting the distortion induced by random lattice coding into a Gaussian noise which holds DP. To do so, they perturb the gradient by Gaussian noise prior to quantization, and the overall procedure then holds DP according to composition theorem of DP. The above works consider DP enhancements, providing users with privacy guarantees from untruthful adversaries, but fail to so for a potential untrusted FL third-party server; as can be guaranteed by LDP.The recent work [34] proposed a compression method that holds LDP. This scheme, referr...

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Survey: federated learning data security and privacy-preserving in edge-Internet of Things

Li,

Ge,

Tian

2024

Artif Intell Rev

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The amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can no longer meet the requirements for training complex models with large amounts of data. Federated learning, as a new paradigm for training statistical models in distributed edge networks, alleviates integration and training problems in the context of massive and heterogeneous data and security protection for private data. Edge computing processes data at the edge layers of data sources to ensure low-data-delay processing; it provides high-bandwidth communication and a stable network environment, and relieves the pressure of processing massive data using a single node in the cloud center. A combination of edge computing and federated learning can further optimize computing, communication, and data security for the edge-Internet of Things. This review investigated the development status of federated learning and expounded on its basic principles. Then, in view of the security attacks and privacy leakage problems of federated learning in the edge Internet of things, relevant work was investigated from cryptographic technologies (such as secure multi-party computation, homomorphic encryption and secret sharing), perturbation schemes (such as differential privacy), adversarial training and other privacy security protection measures. Finally, challenges and future research directions for the integration of edge computing and federated learning are discussed.

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Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Cited by 6 publications

References 202 publications

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

Joint Privacy Enhancement and Quantization in Federated Learning

Survey: federated learning data security and privacy-preserving in edge-Internet of Things

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