A privacy preserving framework for federated learning in smart healthcare systems

Wang, Wenshuo; Li, Xu; Qiu, Xiuqin; Zhang, Xiang; Zhao, Jindong; Brusic, Vladimir

doi:10.1016/j.ipm.2022.103167

Cited by 40 publications

(16 citation statements)

References 41 publications

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“…The efficacy of the framework was evaluated through deep learning applications for COVID-19 patients and demonstrated strong potential for secure and widespread adoption of IoHT-based health management. Wang et al [33] proposed a smart healthcare framework for sharing physiological data, called FRESH, that uses Federated Learning (FL) and ring signature defense to protect against source inference attacks (SIAs). The framework collects physiological data from wearable devices and processes it using edge computing devices for local training of machine learning models.…”

Section: Federated Learning-based Solutionmentioning

confidence: 99%

Digitization of healthcare sector: A study on privacy and security concerns

et al. 2023

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Section: Federated Learning-based Solutionmentioning

confidence: 99%

Digitization of healthcare sector: A study on privacy and security concerns

et al. 2023

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“…Edge computing devices process these data. This architecture in [31] formulates the data sharing challenge as a machine learning problem while integrating privacy-preserving FL. However, the decentralized nature of FL poses novel security challenges that are not effectively addressed in the existing works.…”

Section: Federated Learning-based Iot Attack Detection Approachesmentioning

confidence: 99%

Federated Learning-based Routing Vulnerability Analysis and Attack Detection for Healthcare 4.0

2024

IJIES

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Industrial 4.0 technological breakthroughs highly impact healthcare 4.0 and enable transformative impact on the healthcare system by shifting towards efficient, patient-centric, data-driven, and robust global healthcare services. This paper presents a robust security framework; federated learning (FL) based RPL vulnerability analysis and attack detection (FRVA), for ensuring secure Healthcare 4.0. The FRVA is proposed to defend the RPLhealthcare 4.0 against multiple attacks by applying deep learning-based fuzzing and FL-enabled hybrid learning. RPL vulnerabilities are analyzed using randomly generated inputs by deep learning-based fuzzing. Further, it feeds the RPL vulnerability-rich fuzzed output dataset to the FL-hybrid learning model. The second model improved the customized local learning models using globally shared information according to FL, resulting in high learning accuracy with precise attack detection. The proposed FRVA runs the vulnerability analysis and attack detection at the edges to prolong the network lifetime with high security. Moreover, the performance of the FRVA is validated through Python-based simulations using different metrics. The simulation results demonstrate that the proposed FLbased hybrid CNN-LSTM strategy enhances the accuracy by 5.55% and 12.9%, respectively, compared with the individual CNN and LSTM methods. It also enhances the accuracy by 25.83% and 5.97% than the other conventional FL-based detection strategies.

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“…Independently of the belonging category, none of the above-examined approaches consider the problem of data privacy, which remains a critical concern when handling sensitive information such as diabetic data [ 73 ]. FL technology has been utilized in the medical domain to train a prediction model through decentralized data for dealing with different problems [ 74 , 75 , 76 , 77 ].…”

Section: State Of the Artmentioning

confidence: 99%

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

Falco

Cioppa

Koutný

et al. 2023

Sensors

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In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

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A privacy preserving framework for federated learning in smart healthcare systems

Cited by 40 publications

References 41 publications

Digitization of healthcare sector: A study on privacy and security concerns

Digitization of healthcare sector: A study on privacy and security concerns

Federated Learning-based Routing Vulnerability Analysis and Attack Detection for Healthcare 4.0

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

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