A Secure Federated Learning Framework for 5G Networks

Liu, Yi; Peng, Jialiang; Kang, Jiawen; Iliyasu, Abdullah M.; Niyato, Dusit; El‐Latif, Ahmed A. Abd

doi:10.1109/mwc.01.1900525

Cited by 195 publications

(69 citation statements)

References 17 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.…”

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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“…The blockchain-empowered training data is first uploaded to a decentralized IPFS file system for data provenance, and then either a partially decentralized or a fully decentralized co-operative model training takes place. Blockchain has been proposed by researchers to thwart data poisoning and membership inferencing attacks by not allowing malicious or unreliable FL participants [96] . Blockchain has been used for failure detection in IoHT devices using FL [97] .…”

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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“…This strategy was motivated by multiparty secure computation, which was also investigated in Reference [55]. Besides, Liu et al [56] used smart contracts in the self-defense of FL. Membership inference and poisoning attacks were, thus, prevented in this way.…”

Section: Blockchain-enabled Aimentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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A Secure Federated Learning Framework for 5G Networks

Cited by 195 publications

References 17 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

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

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