Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach

Liu, Yi; Garg, Sahil; Nie, Jiangtian; Zhang, Yang; Xiong, Zehui; Kang, Jiawen; Hossain, M. Shamim

doi:10.1109/jiot.2020.3011726

Cited by 339 publications

(170 citation statements)

References 20 publications

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“…Federated Learning (FL) (McMahan et al 2017 ) will establish a data protection model, distributing dataset on each client machine, and aggregating locally-computed updates for a globally model which helps the participating clients to achieve experimental results similar to distributed data (Liu et al 2020b , 2020b ), while maintaining the privacy of the training data (Liu et al 2020a ). Therefore, as a promising distributed machine learning framework for privacy protection, FL has spawned many emerging applications such as Google Keyboard (Hard et al 2018 ), traffic flow prediction (Liu et al 2020c ), anomaly detection (Liu et al 2021 ; Wu et al 2019 ), medical imaging (Sheller et al 2020 ), etc. In particular, medical institutions turn their attention to FL to develop a collaborative learning paradigm for privacy protection, thereby avoiding legal problems caused by data sharing.…”

Section: Related Workmentioning

confidence: 99%

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

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Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

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Section: Related Workmentioning

confidence: 99%

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

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“…In a similar study, the security and privacy of AI training or inferencing data and models within 6G networks were surveyed in [51] and [52] . The authors in [53] proposed a self-healing FL network, which could collaboratively train and detect anomalous nodes. To address privacy, poisoning attacks, and latency issues, the work presented in [54] used blockchain where miners approved the uploaded models from federated edge nodes through a consensus mechanism.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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“…Accordingly, fault diagnostics are then delivered to manufacturers and/or buildings' end‐users in real‐time or near real‐time. Moving forward, in References [28], anomalies of time‐series data of industrial IoT have been detected using a DNN model that is implemented based on a federated learning approach. The latter does not require to share industrial data with a central entity, since it is very sensitive.…”

Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach

Cited by 339 publications

References 20 publications

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

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