ijamri

International Journal of Advanced Multidisciplinary Research and Innovation (IJAMRI)

E-ISSN: 3107-6157 <!-- • 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.
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.

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Volume 3 ✿ Issue 2
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A Review of Federated Learning: Privacy Preserving Techniques and Real-World Applications

Author(s)

Payal Joshi

Country

India

Abstract

The process of Federated learning (FL) allows different devices to jointly create a model through collaboration without exchanging training information. FL protects diversified applications from sensitive data leakage through mechanics including differential privacy and homomorphic encryption and secure multi-party computation which meet privacy legislation. FL serves diverse healthcare and financial sectors as well as IoT operations alongside edge computing applications because it keeps sensitive information within individual devices. As much as FL provides benefits its implementation faces three main challenges related to communication overhead and non-IID data distributions together with security vulnerabilities.

Keywords

Federated Learning, Privacy-Preserving Machine Learning, Secure Multi-Party Computation, Differential Privacy, Decentralized AI

Published In

Volume 1, Issue 1, March-April 2025

Published On

2025-05-05

E-ISSN: XXXX-XXXX