Blockchain-based federated learning methodologies in smart environments

Liu, Dong; Luo, Zhaohui; Cao, Bo

doi:10.1007/s10586-021-03424-y

Cited by 54 publications

(33 citation statements)

References 76 publications

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“…Blockchain could be used as a solution to IoT security issues. The continuous research of blockchain technology is key to keeping and improving on various high security that can help to solve security and privacy issues in a variety of domains (Li, Luo & Cao, 2022). Just like using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy in developing countries that are currently facing identity theft that is affecting both individual and country images.…”

Section: Telecommunicationsmentioning

confidence: 99%

Review of Issues on Data Security Cybercrimes and Image: The Nigerian Case

2023

IJMCNM

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The paper seeks to address the lapses in policy formulation and implementations on the issue of database in Nigeria and by extension the rest of developing countries that are confronting with various activities of cyber criminals. Most of these government pay leap service to boosting the image of their countries whereas it is in their sole responsibility to monitor communications and detect abuse on networks due to influence of globalization. Up till now, no dependable data base of citizenry in Nigeria and that makes it difficult to check people activities online which resulting to all security issues that presently deforming the country images. Images are measured by performance of invisible indicators. The study seeks on how Nigeria can improve her image by identifying the impact of information security and effective governance with the immediate application of automation system in the country since country's reputation plays a major role in attracting investment and boosting the economy, as reputation negatively affected by the increasing number of cybercrime incidents. Data security and cybercrime in Nigeria are significant and need to be addressed as a matter of urgency by taking proactive steps to improve data security, enhance cybercrime enforcement, and protect the country's image, Nigeria can build a more secure and stable digital environment for its citizens.

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Section: Telecommunicationsmentioning

confidence: 99%

Review of Issues on Data Security Cybercrimes and Image: The Nigerian Case

2023

IJMCNM

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“…where the learning rate is represented by 𝜆.Weights between output and hidden layers are updated utilizing equation above, while weights between input and hidden layers are updated utilizing equation below (17). We select private blockchain to prevent unauthorized sharing by restricting management as well as access of network participants.…”

Section: 1mentioning

confidence: 99%

Blockchain machine learning based personal health record data analysis with smart decentralization and security enhancement

Shah

Rai

Singh

et al. 2023

Preprint

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An essential component of a patient's follow-up is a medical record. It includes opinions, prescriptions, analyses, and all patient data of healthcare professionals. Process of sharing as well as managing this file involves a number of players, including patient, doctor, and pharmacist. Electronic medical record (EMR) can be accessed by any authorized individual from any location, and data are shared among various health service providers. Using blockchain machine learning techniques, this study proposes a novel method for personal health records (PHR) -based data analysis and network security enhancement. Variational Boltzmann spatio encoder neural networks were utilized for the data analysis of personal health records. The decentralized blockchain architecture enhances network security. Based on network security and data analysis, the experimental analysis is conducted in terms of random accuracy 81%, specificity55%, latency 62%, QoS 52%, and computational cost 41%.

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“…Furthermore, the development of FL-AI techniques in healthcare, the applications of Smart technologies in healthcare have been systematically analyzed, and the security and privacy concerns, as well as medical issues have been portrayed. In the suggested taxonomy, we thoroughly examined the healthcare sector with the interconnection of FL-AI [4,83,160].…”

Section: Taxonomies Of Fl With Ai In Healthcarementioning

confidence: 99%

“…Deep Learning (DL) approaches, for example, have shown potential in biomedical image analysis for earlier identification of acute illnesses by managing enormous volumes of relevant data to improve healthcare efficiency [3]. Dong et al [4] suggested a detailed analysis based on blockchain-based FL to discuss the issue of privacy concerns using datasets to identify the current state of this problem. The study suggested that blockchain-based FL has developed significantly over the last five years, and the technology is now being applied to the fields of IoT and smart healthcare applications, such as recording patient health data, medical image analysis, to study data related to analysis of cancer related illness, and relevant economic data.…”

Section: Introductionmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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Blockchain-based federated learning methodologies in smart environments

Cited by 54 publications

References 76 publications

Review of Issues on Data Security Cybercrimes and Image: The Nigerian Case

Review of Issues on Data Security Cybercrimes and Image: The Nigerian Case

Blockchain machine learning based personal health record data analysis with smart decentralization and security enhancement

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

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