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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23421
Article Published : 9054
Total Authors : 24024
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | A PRIVACY-PRESERVING FEDERATED LEARNING FRAMEWORK FOR SECURE DISTRIBUTED MODEL TRAINING |
| Authors Name: | Raushan Kumar , Anurag Shrivastava |
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IJRTI_210690
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| Published Paper Id: | IJRTI2603118 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | With the rapid growth of distributed data generation, traditional centralized machine learning approaches face serious challenges related to data privacy and security. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without sharing raw data among participants. However, recent studies have shown that sensitive information can still be inferred from model updates, making privacy preservation a critical concern in federated learning systems. This paper proposes a privacy-preserving federated learning framework that integrates Differential Privacy, Secure Aggregation, and homomorphic Encryption to enhance data confidentiality during distributed model training. In this work, local models are trained on client devices using private datasets, and controlled noise is added to the model gradients to prevent data leakage. The privacy-preserved updates are then encrypted and securely aggregated at the central server, ensuring that individual client contributions remain confidential. An adaptive privacy budget mechanism is incorporated to balance the trade-off between model accuracy and privacy protection. |
| Keywords: | Federated Learning, Privacy Preservation, Differential Privacy, Secure Multiparty Computation, Homomorphic Encryption |
| Cite Article: | "A PRIVACY-PRESERVING FEDERATED LEARNING FRAMEWORK FOR SECURE DISTRIBUTED MODEL TRAINING", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b117-b120, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603118.pdf |
| Downloads: | 00081 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2603118
Registration ID:210690
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b117-b120 Country: Bhopal, Madhya Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603118 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603118 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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