IDEAS home Printed from https://ideas.repec.org/a/das/njaigs/v6y2024i1p492-502id274.html
   My bibliography  Save this article

The Role of Blockchain in Secure and Scalable Distributed Learning Systems

Author

Listed:
  • Md. Mafiqul Islam
  • , Dr Patrick Zingisa Msekelwa

Abstract

Blockchain technology has emerged as a transformative tool for enhancing security, transparency, and scalability in distributed learning systems. This paper explores how blockchain can address critical challenges in these systems, such as data integrity, trust among participants, and scalability of learning models. By leveraging blockchain's decentralized ledger and smart contract capabilities, it becomes possible to establish secure data sharing, mitigate privacy concerns, and promote collaborative learning without reliance on central authorities. Furthermore, we discuss novel consensus mechanisms optimized for distributed learning, ensuring efficiency and scalability. Real-world applications in sectors like healthcare, finance, and education are also reviewed to highlight the practical benefits of integrating blockchain into distributed learning systems. Finally, the paper identifies open research challenges and future directions to advance this interdisciplinary field.

Suggested Citation

  • Md. Mafiqul Islam & , Dr Patrick Zingisa Msekelwa, 2024. "The Role of Blockchain in Secure and Scalable Distributed Learning Systems," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 492-502.
  • Handle: RePEc:das:njaigs:v:6:y:2024:i:1:p:492-502:id:274
    as

    Download full text from publisher

    File URL: https://newjaigs.com/index.php/JAIGS/article/view/274
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maher Gerges & Ahmed Elgalb, 2024. "Comprehensive Comparative Analysis of Mobile Apps Development Approaches," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 430-437.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prashis Raghuwanshi, 2024. "AI-Driven Identity and Financial Fraud Detection for National Security," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 38-51.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:das:njaigs:v:6:y:2024:i:1:p:492-502:id:274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Open Knowledge (email available below). General contact details of provider: https://newjaigs.com/index.php/JAIGS/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.