International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 5
September-October 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Federated Learning Frameworks for Secure and Decentralized Authentication

Author(s) Merlin Balamurugan
Country United States
Abstract Federated Learning (FL) is a cutting-edge machine learning approach that enables multiple or edge devices to train models collaboratively without sharing sensitive data. This approach not only ensures the privacy and security of data by keeping it localized but also promotes the collective improvement of machine learning models across various participants. A systematic literature review explored integrating Blockchain technology with federated learning. Blockchain's potential to address existing security and privacy vulnerabilities in traditional federated learning systems is analyzed in depth. One of the key benefits of combining Blockchain with FL is enhanced protection against potential attacks, such as data tampering or unauthorized access. The study also examines how Blockchain-based federated learning systems can offer better records and rewards management, contributing to fairer and more transparent systems. In addition, Blockchain's role in improving verification and accountability within federated learning frameworks has been critically evaluated. By integrating Blockchain, federated learning can achieve higher levels of trust and security in collaborative machine-learning processes. The latest research highlights innovative Blockchain-based methods that tackle these challenges, ensuring robust privacy and security measures. Overall, this approach represents a significant advancement in distributed machine learning, aligning with contemporary needs for data protection and collaborative efficiency.
Keywords Federated Learning, Distributed machine learning, Blockchain, Smart contract, Privacy and Security
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-09-26
Cite This Federated Learning Frameworks for Secure and Decentralized Authentication - Merlin Balamurugan - IJFMR Volume 6, Issue 5, September-October 2024.
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4Cover Page Vol 6 Isu 3

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com