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 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

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 for Private Health Data Training

Author(s) Vilas N
Country India
Abstract The growing reliance on Artificial Intelligence (AI) in healthcare has brought about significant challenges related to privacy and security of patient data. Federated learning (FL) has emerged as a promising solution to these challenges by enabling collaborative model training without sharing sensitive data. This paper explores the application of federated learning in private health data training, outlining its principles, advantages, challenges, and future directions. Federated learning enables multiple institutions to collaborate in training machine learning models while keeping the data decentralized and protected. By maintaining data at local sites and sending only model updates to a central server, FL ensures that sensitive health information remains private and secure. The paper discusses how FL can be applied to a wide range of healthcare domains, including electronic health records (EHR), medical imaging, and predictive analytics. However, challenges such as data heterogeneity, communication inefficiencies, scalability issues, and the risk of adversarial attacks must be addressed. Additionally, ethical concerns related to patient consent, regulatory compliance, and data ownership are explored. The paper concludes by highlighting the potential of federated learning to revolutionize the way healthcare data is utilized for AI model training while preserving privacy, and it suggests ways to overcome the barriers for broader adoption in the healthcare sector.
Field Medical / Pharmacy
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41434
Short DOI https://doi.org/g9fm3j
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 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

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 +91-9687-828-838 editor@ijfmr.com