Blockchain based Federated Learning approach for Detection of COVID- 19 using Io MT

Wadhwa, Shivani; Saluja, Kamal; Gupta, Sunil; Gupta, Divya

doi:10.2139/ssrn.4159195

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Additionally, relying on encryption technologies and consensus algorithms of distributed systems, blockchain as a distributed ledger technology can effectively solve the problem of security vulnerabilities caused by centralized nodes [14]. Since FL technologies can provide privacy protection for users [20] and blockchain technologies can ensure the data security of users [21], some works have been conducted for designing a blockchain-empowered FL framework for smart healthcare [22]- [24]. Chang et al [22] proposed a blockchainbased FL framework for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and IoMT devices implement the FL to make full of distributed clinical data.…”

Section: Related Workmentioning

confidence: 99%

“…Jatain et al [23] proposed a blockchain-based FL framework for the secure aggregation of private healthcare data, which can provide an efficient method to train machine learning models. Wadhwa et al [24] proposed a blockchainbased FL approach for the detection of patients using IoMT devices, which provides security for the detection of patients. However, most works do not consider how to incentivize users to contribute fresh sensing data for reliable healthcare services, especially under information asymmetry.…”

Section: Related Workmentioning

confidence: 99%

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Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Kang

Nie

et al. 2022

2022 IEEE International Conference on Blockchain (Blockchain)

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Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a usercentric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a crosschain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical crosschain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a usercentric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Kang

Nie

et al. 2022

2022 IEEE International Conference on Blockchain (Blockchain)

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“…It aims to make federated learning efficient and flexible in contexts with minimal resources [ 19 ]. It could be used in many different domains that require fast analysis of large amounts of distributed data [ 20 ]. In the field of FL, there is still a lack of studies on MDD classification.…”

Section: Introductionmentioning

confidence: 99%

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Gupta,

Khullar,

Goyal

et al. 2023

Diagnostics

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In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

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Comparing the performance of machine learning algorithms using estimated accuracy

Gupta¹,

Saluja²,

Goyal³

et al. 2022

Measurement: Sensors

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Blockchain based Federated Learning approach for Detection of COVID- 19 using Io MT

Cited by 4 publications

References 6 publications

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Comparing the performance of machine learning algorithms using estimated accuracy

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